Hybrid Intelligent Systems

WHAT IS A HYBRID INTELLIGENT SYSTEM?

• An intelligent system that combines at least two intelligent technologies.

Examples:Neuro-Fuzzy SystemsANFIS: Adaptive Neuro-Fuzzy Inference SystemEvolutionary Neural NetworksFuzzy Evolutionary Systems

SOFT COMPUTING

• An emerging approach to building hybrid intelligent systems capable of reasoning and learning in an uncertain and imprecise environment.• It is capable of operating with uncertain,

imprecise and incomplete information in a manner that reflects human thinking.• It attempts to model our sense of words in

decision making.

WHAT DO WE COMBINE IN A HYBRID SYSTEM?

• Hybrid intelligent system can be good or bad• It depends on the components that constitute

the hybrid.• Each component has its own strengths and

weaknesses.• Probabilistic reasoning is mainly concerned with

uncertainty, fuzzy logic with imprecision, neural networks with learning and evolutionary computation with optimization.

• A good hybrid system brings the advantages of different technologies.

COMPARISON OF DIFFERENT INTELLIGENT TECHNOLOGIES

ES FS NN GA

Knowledge Representation

Uncertainty Tolerance

Imprecision Tolerance

Adaptability

Learning Ability

Explanation Ability

Knowledge Discovery and Data MiningMaintainability

LEGEND:

ES – Expert Systems

FS – Fuzzy Systems

NN – Neural Networks

GA – Genetic Algorithms

The terms used for grading are: bad rather bad rather good good

NEURAL EXPERT SYSTEMS

• Expert systems and neural networks share common goals. They both attempt to imitate human intelligence and eventually create an intelligent machine. However, they use very different means to achieve their goals. • Expert Systems – rely on logical inferences, decision trees

and focus on modeling human reasoning.• Neural networks – rely on parallel processing and focus on

modeling a human brain.• Expert systems treat the brain as a black-box, whereas neural

networks look at its structure and functions, particularly at its ability to learn.

NEURAL EXPERT SYSTEMS

• Knowledge in neural networks is stored as synaptic weights between neurons. This knowledge is obtained during the learning phase when a training set of data is presented to the network. The network propagates the input data from layer to layer until the output data is generated. If it is different from the desired output, an error is calculated and propagated backwards through the network. The synaptic weights are modified as the error is propagated. Unlike expert systems, neural network learn without human intervention.

NEURAL EXPERT SYSTEMS

• However, in expert systems, knowledge can be divided into individual rules and the user can see and understand the piece of knowledge applied by the system. In contrast, in neural networks, one cannot select a single synaptic weight as a discrete piece of knowledge. Here knowledge is embedded in the entire network; it cannot be broken into individual pieces, and any change of a synaptic weight may lead to unpredictable results. A neural network is, in fact, a black-box for its user.