07-05-2013, 02:36 PM
Hybrid intelligent systems: Neural expert systems and neuro-fuzzy systems
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Introduction
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, an emerging approach to building hybrid intelligent systems capable of reasoning and learning in an uncertain and imprecise environment.
Although words are less precise than numbers, precision carries a high cost. We use words when there is a tolerance for imprecision. Soft computing exploits the tolerance for uncertainty and imprecision to achieve greater tractability and robustness, and lower the cost of solutions.
We also use words when the available data is not precise enough to use numbers. This is often the case with complex problems, and while “hard” computing fails to produce any solution, soft computing is still capable of finding good solutions.
Neural expert systems
Expert systems rely on logical inferences and decision trees and focus on modelling human reasoning. Neural networks rely on parallel data processing and focus on modelling a human brain.
Expert systems treat the brain as a black-box. Neural networks look at its structure and functions, particularly at its ability to learn.
Knowledge in a rule-based expert system is represented by IF-THEN production rules. Knowledge in neural networks is stored as synaptic weights between neurons.
Approximate reasoning
In a rule-based expert system, the inference engine compares the condition part of each rule with data given in the database. When the IF part of the rule matches the data in the database, the rule is fired and its THEN part is executed. The precise matching is required (inference engine cannot cope with noisy or incomplete data).
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.
Neuro-fuzzy systems
Fuzzy logic and neural networks are natural complementary tools in building intelligent systems. 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. 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.
Neuro-fuzzy systems: summary
The combination of fuzzy logic and neural networks constitutes a powerful means for designing intelligent systems.
Domain knowledge can be put into a neuro-fuzzy system by human experts in the form of linguistic variables and fuzzy rules.
When a representative set of examples is available, a neuro-fuzzy system can automatically transform it into a robust set of fuzzy IF-THEN rules, and thereby reduce our dependence on expert knowledge when building intelligent systems.