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Input /Output

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Question 1
- Hybrid intelligent system is a system that employs a combination of methods from all artificial intelligence subfields.

- An example of hybrid is a neuro-fuzzy system.

- Fuzzy Computing, Neural Computing and Genetic Algorithm are the what constitutes the core of soft computing.

- The difference between soft computing and hard computing is that soft computing has the features of approximation and dispositionality while hard computing has the features of exactilude or precision and categoricity.

Question 2

- Neural expert system is capable of approximate reasoning because it allows dealing with noisy and incomplete data because of its capability of generalization.

Question 3

- Fuzzy systems and neural networks are considered to be natural complementary tools for building intelligent systems because they are natural complementary tools in building intelligent systems. Neural networks are low-level computational structures that perform well when dealing with raw data. Meanwhile fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts.

- The neuro-fuzzy system corresponds to a fuzzy model of Takagi–Sugeno, wherein the weights of the ANN model are similar to the parameters of the fuzzy system. This structure is called ANFIS. It is a hybrid model composed of a fuzzy and artificial neural network. The ANFIS structure consists of five layers, namely, fuzzy layer, product layer, normalized layer, de-fuzzy layer, and total output layer.

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Q1- Hybrid Intelligent system is a system that combines minimum of 2 intelligent technologies. For example, hybrid neuro-fuzzy system is produced from the combination of neural network and fuzzy system.

Core of soft computing is the combination of probabilistic reasoning, fuzzy logic, neural networks and evolutionary computation.

Differences between hard and soft computing is that hard computing is not able to produce solutions whereas soft computing are able to find good solutions very well. Other than that, hard computing is by binary logic while soft computing is based of a model of human mind.

Q2- Neural expert capable of approximate reasoning because the information flow in the system is controlled and the initiates inference over the neural knowledge base.

Q3- Fuzzy systems and neural networks considered to be natural complementary tools for building intelligent systems because fuzzy logic is high level reasoning and using linguistic information acquired from domain experts.

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Q1

A hybrid intelligent system is one that
combines at least two intelligent
technologies.For example,combining a neural
network with a fuzzy system results in a
hybrid neuro-fuzzy system.

The combination of
probabilistic reasoning,
fuzzy logic, neural
networks, and
evolutionary computation
forms the core of soft
computing.

Hard computing takes a
lot of time in computing
as it requires the stated
analytical model and the
model soft computing is
based on is that of human
intelligence.The combination of
probabilistic reasoning,
fuzzy logic, neural
networks, and
evolutionary computation
forms the core of soft
computing.

Q2

the neural expert system
use a trained neural
network in place of the
knowledge base. the input
data does not have to
precisely match the data
that was ised in network
training.

Q3

Neural networks are low-
level computational
structures that perform
well when dealing with
raw data, while fuzzy logic
deals with reasoning on a
higher level, using
linguistic information
acquired from domain
experts.

# data[].attributes.headline
AHMAD FADLI BIN MOHD ZAHARI(056623)        Q2 

# data[].attributes.subject
AHMAD FADLI BIN MOHD ZAHARI(056623)        Q2 

# data[].attributes.body
QUESTION 1 : What is a hybrid intelligent system? Give an example. What constitutes the core of soft computing? What are the differences between ‘hard’ and ‘soft’ computing?

A hybrid intelligent system is one that combines at least two intelligent technologies. For example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system. The constitutes of the core of soft computing is the combination of probabilistic reasoning, fuzzy logic, neural networks and evolutionary. The differences between har and soft computing is when hard computing fails to produce any solution, soft computing is still capable of finding good solutions.

QUESTION 2: Why is a neural expert system capable of approximate reasoning? Draw a neural knowledge base for a three-class classification problem. Suppose that an object to be classified is either an apple, an orange or a lemon.

Because it has neural inference engine where it controls the information flow in the system and initiates inference over the neural knowledge base.

QUESTION 3: Why are fuzzy systems and neural networks considered to be natural complementary tools for building intelligent systems? Draw a neuro-fuzzy system corresponding to the Sugeno fuzzy inference model for the implementation of the AND operation. Assume that the system has two inputs and one output, and each of them is represented by two linguistic values: small and large.

Because while neural networks are low-level computational structures that perform well when dealing with raw data, fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts.

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Question 1:
- Hybrid intelligent system is a system where  it has a combination of at least two intelligent technologies such as Neural Network(NN) + Fuzzy System(FS)
- Hard computing are used to solve mathematical problem while soft computing are used to solve real world problems
- core fore soft computing is probabilistic reasoning, fuzzy logic, neural networks and evolutionary computation.

Question 2:
- the neural expert system use a trained neural network in place of the knowledge base. the input data does not have to precisely match the data that was ised in network training.

Question 3:
- Neural networks are low-level computational structures that perform well when dealing with raw data, while fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts.

# data[].attributes.body
1.A hybrid intelligent system is one that combines at least
two intelligent technologies.For example, combining a neural network with a fuzzy
system results in a hybrid neuro-fuzzy system

Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and evolutionary algorithms, which can be used to produce powerful hybrid intelligent systems.

Soft computing relies on formal logic and probabilistic reasoning while hard computing relies on binary logic and crisp system.

Soft computing helps users to solve real-world problems by providing approximate results that conventional and analytical models cannot solve.

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Draw a neural knowledge base for a three-class classification problem.

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Hybrid intelligent system is one that combines  at least two intelligent technologies that is framed by combining at least two intelligent technologies like Fuzzy Logic, Neural networks and Genetic algorithms.

Core of soft computing is an emerging approach to building hybrid intelligent  systems capable of reasoning and learning in an  uncertain and imprecise environment. Fuzzy Computing, Neural Computing, Genetic Algorithms are considered as core of soft computing.

Soft Computing relies on formal logic and probabilistic reasoning while Hard computing relies on binary logic and crisp system.

Question 2
Neural expert system is the inference engine so It controls the information flow in the system and initiates inference over the neural knowledge base. Neural network also allows dealing with noisy and incomplete data because its capability. Hence it allows approximate reasoning.

Question 3
Fuzzy logic and neural networks are natural complementary tools in building intelligent systems. Neural networks are low-level computational structures.  Neural networks are low-level computational structures that perform well when dealing with raw data, fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts.

# data[].attributes.body
Question 1:
A hybrid intelligent system is a combination of at least two intelligent technologies. For example, Neural Network(NN) + Fuzzy System(FS) results in Hybrid Neuro-Fuzzy System

Core of soft computing is formed by the combination of probabilistic reasoning, fuzzy  logic, neural networks and evolutionary  computation.

Hard computing relies on binary logic and predefined instructions suck as numerical analysis and brisk software and uses two-valued logic.

Soft computing is based on model of human mind where probabilistic reasoning, fuzzy logic, and uses multivalued logic.

Question 2:
Neural Expert System ensures approximate reasoning by which controls the information flow in the system and initiates inference over the neural knowledge base. The input data does not have to precisely match the data that was used in network training. This ability is called approximate reasoning.

Question 3:
Fuzzy Logic deals with reasoning on a higher level, using linguistic information acquired from domain experts.
Neural Networks are low-level computational structures that perform well when dealing with raw data.

# data[].attributes.body
Question 1
- The hybrid intelligent system is one that combines at least two intelligent technologies.  For example,  combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system.

- The combination of probabilistic reasoning, fuzzy  logic, neural networks, and evolutionary  computation forms the core of soft computing

- Soft computing is capable of finding good solutions to complex problems while hard computing fails to produce any solution.

Question 2
Neural expert systems use a trained neural network in place of the knowledge base. The input data does not have to precisely match the data that was used in network training. This ability is called approximate reasoning.

Question 3
Neural networks are low-level computational structures that perform well when dealing with raw data, fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts.  However, fuzzy systems lack the ability to learn and cannot adjust themselves to a new environment.  On the other hand, although neural networks can learn, they are opaque to the user.

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Question 1
A hybrid intelligent system is a combination of at least 2 intelligent technologies. It may varies for an example neural network with fuzzy system as result in a hybrid neuro-fuzzy system.

The core of soft computing is fuzzy computing, neural computing and genetic algorithm. With soft computing, it provides solution that may have not been properly.

Hard computing vs soft computing :
-Hard computing usually being use to solve mathematical problem while soft computing is better and usually use to solve real-world problems.
-Hard computing depends on binary logic as it use numerical analysis while soft computing is refer the human mind which contains fuzzy logic.
-Hard computing needs the exact and precise data and must sequential while soft computing can handle big data and more complex computation.

Question 2
The reason why neural expert system is capable of approximate reasoning because neural network gives the solution to deal with the noisy and the incomplete data and it also capable doing generalisation of data.

Question 3
Neural network works super well when dealing with raw data and do some low-level computational structures. On the other hand, fuzzy logic deals with reasoning on higher level computational.
[diagram jap lagi]

# data[].attributes.headline
Muhammad Hafiz Zul Arief Bin Mohd Radzi - 056534

# data[].attributes.subject
Muhammad Hafiz Zul Arief Bin Mohd Radzi - 056534

# data[].attributes.body
Q1

-Hybrid intelligent system when there are at least 2 combination of intelligent technologies. For example hybrid neuro-fuzzy system.

-The core of soft computing is a combination  of probabilistic reasoning, fuzzy logic, neural network and evolutionary computation.

-When it comes to complex problems, hard computing fails to produce any solution but the soft computing still can find good solutions.

Q2
-Neural expert system capable of approximate because of generalisation. the input data does not need to match the data in training network which is why it can deal with noisy and incomplete data.

Q3
-fuzzy systems and neural networks are considered to be natural complementary tools because neural network are low-level computational that perform well with raw data and fuzzy system deal with reasoning on a higher level

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QUESTION 1
1.A hybrid intelligent system is one that combines  at least two intelligent technologies.  For example,  combining a neural network with a fuzzy system  results in a hybrid neuro-fuzzy system.

2.The combination of probablistic reasoning,fuzzy logic,neural networks and evulutionary computation forms the core of soft computing

3.Soft Computing could be a computing model evolved to resolve the non-linear issues that involve unsure, imprecise and approximate solutions of a tangle. These sorts of issues square measure thought of as real-life issues wherever the human-like intelligence is needed to resolve it.

Hard Computing is that the ancient approach employed in computing that desires Associate in Nursing accurately declared analytical model.

Question 2
The combination of the rule extraction component and the neural network enables the neural expert system to justify and explain its conclusion. Because of its ability to generalise, neural networks can also deal with noisy and incomplete data. As a result, it allows for approximate reasoning.

Question 3
Fuzzy logic and neural networks are natural complements in the development of intelligent systems. While neural networks are low-level computational structures that perform well with raw data, fuzzy logic deals with higher-level reasoning by utilising linguistic information obtained from domain experts.

An adaptive neuro-fuzzy inference system, also known as an adaptive network-based fuzzy inference system (ANFIS), is a type of artificial neural network based on the Takagi–Sugeno fuzzy inference system. It has the potential to capture the benefits of both neural networks and fuzzy logic principles because it integrates both in a single framework. Its inference system is represented by a set of fuzzy IF–THEN rules with the ability to learn and approximate nonlinear functions. As a result, ANFIS is regarded as a universal estimator. The best parameters obtained by genetic algorithm can be used to use the ANFIS in a more efficient and optimal manner.

# data[].attributes.headline
Mohammad Naim Ashraff Bin Mazuki (055725)

# data[].attributes.subject
Mohammad Naim Ashraff Bin Mazuki (055725)

# data[].attributes.body
Question 1

-A hybrid intelligent system is one that combines  at least two intelligent technologies.
Example,  combining a neural network with a fuzzy system  results in a hybrid neuro-fuzzy system.

-The combination of probabilistic reasoning, fuzzy  logic, neural networks and evolutionary  computation forms the core of soft computing.

-Hard computing fails to produce any  solution, soft computing is still capable of  finding good solutions.

Question 2

-Neural expert systems use a trained neural network in  place of the knowledge base. The input data does not  have to precisely match the data that was used in  network training. This ability is called approximate reasoning.

Question 3

-Fuzzy logic deals with  reasoning on a higher level, using linguistic  information acquired from domain experts.

-Neural networks are low-level  computational structures that perform well when  dealing with raw data.

# data[].attributes.headline
Siti nursyazwanie izzati binti mewahna (060293)

# data[].attributes.subject
Siti nursyazwanie izzati binti mewahna (060293)

# data[].attributes.body
Question 1
- Hybrid intelligent system is a combination of at least two intelligent technologies. Example, hybrid neuro-fuzzy system that is a combination of neural network system and fuzzy system.

- Probabilistic reasoning, fuzzy logic, neural networks and evolutionary computations.

- Soft : resolve issues that involve uncertain and imprecise environment to achieve greater tractability and robustness
Hard : hard computing fails to produce any solution and relies on binary logic

Question 2
- Neural expert system capable of approximate reasoning because it used a trained neural network in place of knowledge base.

Question 3
Neural networks are low-level  computational structures that perform well when  dealing with raw data, fuzzy logic deals with  reasoning on a higher level, using linguistic  information acquired from domain experts

What problem can we fix using ANFIS?
ANFIS is used when there is uncertain problem that has no precise way of solving it. This kind of problem usually relate with human behavior.

Summary of ANFIS
ANFIS is a artificial neural network that are based on Sugeno fuzzy model. ANFIS use hybrid learning algorithm that combine the least square estimator and the gradient descent method. Jang suggest that both antecedent and consequent parameter are optimised. In forward pass, consequent parameter are adjusted while antecedent parameter are remain fixed. However in backward pass, the consequent parameter are tuned while consequent parameter are kept fixed.

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Q1
-The term "hybrid intelligent system" refers to a system that incorporates at least two intelligent technologies. Combining a neural network with a fuzzy system, for example, yields a hybrid neuro-fuzzy system.

-The basis of soft computing is a blend of probabilistic reasoning, fuzzy logic, neural networks, and evolutionary computation.

-Hard computing is based on binary logic and predetermined instructions, such as numerical analysis and fast software, and it employs two-valued logic. Soft computing is built on a human mind model that includes probabilistic reasoning, fuzzy logic, and multivalued logic.

Q2
-Because it controls the input flow in the system and conducts inference across the neural knowledge base, a neural expert system is capable of approximate reasoning.

Q3
-Because fuzzy logic deals with higher-level reasoning using linguistic input obtained from domain experts, fuzzy systems and neural networks are regarded as ideal complementing methods for developing intelligent systems. Fuzzy systems, on the other hand, lack the ability to learn and adapt to new situations.

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Question 1

1. Hybrid Intelligent system is the combines at least two intelligent technologies. for example combining a neural network with a fuzzy system result in a hybrid neuro-fuzzy system

2. the combination probabilistic of reasoning, fuzzy logic , neural network and evolutionary computation

3.
hard computing - fails to produce any solution
soft computing - still capable of finding good solutions

Question 2

Neural expert systems use a trained neural network in place of the knowledge base. The input data does not have to precisely match the data that was used in network training

Question 3

Neural network are low-level computational structures that perform well when dealing with raw data and fuzzy logic deals with reasoning on a higher level , using linguistic information acquired from domain experts.

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Qusetion 1

- Hybrid intelligence system is combination of at least 2 intelligent technology. For example combination of neural network and fuzzy system.

- Soft computing exploits the tolerance for uncertainly and imprecision to achieve greater tractability and robustness

- Hard computing fails to produce any solution while soft computing still capable of finding good solution

Question 2

Neural expert system capable of approximate reasoning because it is inference engine that controls the data flow in the system and initiates inference over knowledge network. The input data does not have to precisely match the data that was used.

Question 3

Fuzzy system and neural network considered to be natural complementary because neural network can perform well when dealing with the raw data because it is low level computation structures and fuzzy logic deal with reasoning on higher level using linguistic information.

ANFIS
An ANFIS apply hybrid learning algorithm that combine the least-squares estimator and the gradient descent method. In training algorithm each epoch composed form forward pass and backward pass.  In forward pass, training set of input pattern is presented to ANFIS neuron outputs are calculated on the layer by layer and rule consequent parameter are identified.  In backwards pass the back propagation algorithm is applied the error signals are propagated back and antecedent parameters are updated according to chain rule.

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1) Hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence subfields, such as: Neuro-symbolic systems. Neuro-fuzzy systems. Reinforcement learning with fuzzy, neural, or evolutionary methods as well as symbolic reasoning methods.

- Soft computing is tolerant of imprecision, uncertainty, partial truth, and approximation. Hard computing signifies precision and categoricity, whereas soft computing has tolerance of approximation and dispositionality.

- Hard computing is based on solid binary logic which is easy to understand and explainable whereas soft computing is based on heuristic programming, fuzzy logic, neural nets and probabilistic reasoning.

2) The combination of rule extraction component along with neural network enables neural expert system to justify and provide explanation facility for its conclusion. Neural network also allows dealing with noisy and incomplete data because of its capability of generalisation. Hence it allows approximate reasoning.

3) The rule base of a fuzzy system is interpreted as a neural network. Fuzzy sets can be regarded as weights whereas the input and output variables and the rules are modeled as neurons. Neurons can be included or deleted in the learning step. Finally, the neurons of the network represent the fuzzy knowledge base.

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Question 1
Hybrid intelligent system is a combination of 2 intelligent technologies. For example,  combining a neural network with a fuzzy system  results in a hybrid neuro-fuzzy system.

Combination of probabilistic reasoning, fuzzy  logic, neural networks and evolutionary  computation forms the core of soft computing.

Hard computing fails to produce any  solution, while soft computing is still capable of  finding good solutions.

Question 2
Neural expert system capable of approximate reasoning because it use a trained neural network in  place of the knowledge base. The input data does not  have to precisely match the data that was used in  network training.

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Al-Afiq Nur Qayyum Bin Abd Rashid(055662)

# data[].attributes.subject
Al-Afiq Nur Qayyum Bin Abd Rashid(055662)

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Question 1
A hybrid intelligent system is one that combines  at least two intelligent technologies.For example,combining a neural network with a fuzzy system  results in a hybrid neuro-fuzzy system.

The combination of probabilistic reasoning, fuzzy  logic, neural networks and evolutionary  computation forms the core of soft computing

hard computing fails to produce any  solution, soft computing is still capable of  finding good solutions.

Question 2
Because it controls the input flow in the system and conducts inference across the neural knowledge base, a neural expert system is capable of approximate reasoning.

Question 3

Because fuzzy logic deals with higher-level reasoning using linguistic input obtained from domain experts, fuzzy systems and neural networks are regarded ideal complementing methods for developing intelligent systems.
Fuzzy systems, on the other hand, lack the ability to learn and adapt to new situations.

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Amirah Batrisyia binti Abdul Wahab (055475)

# data[].attributes.subject
Amirah Batrisyia binti Abdul Wahab (055475)

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Q1
1) Hybrid intelligent system is a combination of, at least 2 intelligent technologies. For instance, combination of neural network and fuzzy system produces a hybrid neuro-fuzzy system.

2) Core of soft computing are the combination of probabilistic reasoning, fuzzy logic and evolutionary computation.

3) Soft computing can tolerate uncertainty and imprecision to achieve greater tractability and robustness, and lower solution cost. Hard computing produce no result in solving complex problem compared to soft computing.

Q2
Because there is rule-based expert system which is represented by IF-THEN production rules and neural network's knowledge is stored as synaptic weights between neuron. Input data does not have to precisely match the data that was used in network training. This ability is called as approximate reasoning.

Q3
Neural networks (NN) are low-level computational structures that deal with raw data while fuzzy logic is a high-level computational structure deals wit reasoning that uses linguistic information acquired from domain experts. Disadvantages of NN is it is opaque to user while fuzzy logic have trouble to learn and adjusting to new environment. By combining both, it can make up for each of their advantages. As a result, NN becomes more transparent and fuzzy logic adapt the learning ability.

ANFIS uses a hybrid learning algorithm, which combines least-squared estimator and the gradient descend method. Each epoch is made up of forward pass and backward pass. During the forward pass, the ANFIS is presented with a training set of input (an input vector), neuron outputs are calculated layer by layer, and the rule consequent parameter are identified.

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Anisa Irsyahidah Binti Ahmad Ridzuan (055140)

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Anisa Irsyahidah Binti Ahmad Ridzuan (055140)

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QUESTION 1

Hybrid Intelligent : Hybrid Intelligent is a combination of at least 2 intelligent technologies.

Example : Neural Network + Fuzzy System -> Hybrid Neuro-Fuzzy System

Core Soft Computing : An emerging approach to building hybrid intelligent systems capable of reasoning and learning in uncertain and imprecise environment.

Differences between hard and soft computing : Soft Computing relies on formal logic and probabilistic reasoning while Hard Computing relies on binary logic and crisp system

QUESTION 2
It used a trained neural network in place of knowledge base. The input does not have to precisely match the data that was used in network training.

QUESTION 3
While neural networks are low-level computational structures that perform well when dealing with raw data, fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts

SUMMARY OF ANFIS
The least-squares estimator and the gradient descent method are combined in ANFIS' hybrid learning algorithm. The least-squares estimator gives the least value for the sum of squared errors. Each epoch in the ANFIS training process is made up of a forward pass and a backward pass, just like in a Neural Network. A training set of input patterns or an input vector is supplied to the ANFIS in the forward pass, and neuron outputs are calculated layer by layer, with rule consequential parameters discovered.

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QUESTION 1:
1) Combining at least two intelligent technologies. for example, neural network combine with fuzzy system will result in a hybrid neuro-fuzzy system.

2) Combination of probabilistic reasoning, neural networks and evolutionary computations

3) Soft Computing- exploits tolerance for uncertainty and imprecision to achieve greater tractability and robustness, also lower the the cost of solutions
Hard Computing- using words when data not precise enough to use number. fails to produce any solutions.

QUESTION 2:
Because it use a trained neural network in place of the knowledge base  the input data does not have to precisely match the data that was used in network training.

QUESTION 3:
Because they can combine the parallel computation and learning abilities of neutral networks with the human-like knowledge representation and explanation abilities of fuzzy systems. so, neural network become more transparent and fuzzy become capable of learning.

Summary of ANFIS:
ANFIS uses hybrid learning algorithm that combines the least-squares estimator and the gradient descent method. Each epoch is composed from a forward pass and backward pass. In forward pass, a training set of input patterns is presented to the ANFIS. Neuron outputs are calculated on the layer-by-layer basis, and rule consequent parameters are identified. The rule consequent parameters are identified by the least-squares estimator. Least-square estimator gives the least value for the sum of squad errors. ANFIS modeling technique can be used as a viable alternative to carry out analysis without conducting actual experiments which might be very expensive and time consuming process. The system was found to be very flexible and easy to use. Modeling using ANFIS techniques was proved to be very cost effective and practical alternative to the conventional methods.

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Question 1

A hybrid intelligent system is one that combines at least two intelligent technologies. For example, a hybrid neuro-fuzzy system combines a neural network with a fuzzy system.

The combination of probabilistic reasoning, fuzzy logic, neural networks, and evolutionary computation forms the core of soft computing.

Hard computing takes a lot of time in computing as it requires the stated analytical model and the model soft computing is based on is that of human intelligence.

Question 2

because it controls the information flow in the system and initiates inference over the neural knowledge base.

Question 3

because neural networks are low-level computational structures that perform well when dealing with raw data, while fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts.

# data[].attributes.body
QUESTION 1
- A hybrid intelligent system is one that combines at least two intelligent technologies.

- The combination of probabilistic reasoning, fuzzy  logic, neural networks, and evolutionary  computation forms the core of soft computing,

- Soft computing is based on the model of the human mind where it has probabilistic reasoning and fuzzy logic.
- Hard computing relies on binary logic and predefined instructions like numerical analysis and software.

QUESTION 2
- The use of a rule extraction component in combination with a neural network allows the neural expert system to justify and explain its conclusions. Because of their ability to generalise, neural networks can also deal with noisy and incomplete input. As a result, approximation reasoning is allowed.

QUESTION 3
- When dealing with raw data, neural networks are low-level computing structures that function well. Fuzzy logic is involved with higher-level reasoning based on linguistic data gathered from domain expertise.

Summary of ANFIS

ANFIS uses a hybrid learning algorithm that combines the least-square estimator and the gradient descent method. Neuron output is calculated on a layer-by-layer basis, and the rule consequent parameters are identified. Each epoch is composed from a forward pass and a backward pass.Researchers have discovered that the ANFIS modelling technique can be utilised as a feasible alternative to conducting experiments while doing analysis.

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QUESTION 1
Hybrid Intelligent System is one that combines at least two intelligent technologies. For example, combining a neural network with a fuzzy system results in a hybrid neuro fuzzy system.

Probabilistic reasoning, fuzzy logic, neural networks and evolutionary  computation are the core for soft computing.

“hard” computing fails to produce any  solution, soft computing is still capable of  finding good solutions.

QUESTION 2
Neural expert system capable of approximate reasoning because it controls the information flow in the system and initiates inference over the neural knowledge base.

QUESTION 3
Fuzzy systems and neural networks considered to be natural complementary tools for building intelligent systems because fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts.  However, fuzzy systems lack the ability to learn and cannot adjust themselves to a new environment.

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tekan ADD (+) dan tulis nama matrik. untuk soalan kedua & seterusnya.. sila add pada post masing2.

# data[].attributes.subject
tekan ADD (+) dan tulis nama matrik. untuk soalan kedua & seterusnya.. sila add pada post masing2.

# data[].attributes.body
QUESTION 1

- Hybrid intelligent system is a combination of at least two intelligent technologies. For example, combining a neural network with a fuzzy system results a hybrid neuro-fuzzy system.

- The combination of probabilistic reasoning, fuzzy logic, neural networks, and evolutionary computation are the core of soft computing

- "hard" computing fails to produce any solution while "soft" computing is still capable of finding good solutions

QUESTION 2

Neural expert systems use a trained neural network in place of the knowledge base. The input data does not have to precisely match the data that was used in network training.

QUESTION 3

Neural networks are low-level computational structures that perform well when dealing with raw data, fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts.

SUMMARY OF ANFIS

ANFIS implements a hybrid learning algorithm that combines the least-squares estimator with the gradient descent method. Each epoch in the ANFIS training algorithm is made up of a forward pass and a backward pass. During the forward pass, the ANFIS is presented with a training set of input patterns, neuron outputs are calculated layer by layer, and rule consequent parameters are identified. The least-squares estimator is used to determine the rule's subsequent parameters.

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Q1
*: Hybrid intelligent system is system that combine at least two intelligence technologies in it. For example hybrid neuro fuzzy system

*: The combination of probabilistic reasoning, fuzzy logic, neural network and evolutionary computation

*: Hard computing relies on predefined instructions like a numerical analysis and brisk software and uses two-valued logic. Soft computing is based on the model of the human mind where it has probabilistic reasoning, fuzzy logic, and uses multivalued logic

Q2
*:The combination of rule extraction component along with neural network enables neural expert system to justify and provide explanation facility for its conclusion. Neural network also allows dealing with noisy and incomplete data because of its capability of generalisation.

*: