By Zhang Ch.
Fixing advanced difficulties in real-world contexts, comparable to monetary funding making plans or mining huge facts collections, comprises many alternative sub-tasks, each one of which calls for assorted suggestions. to house such difficulties, a superb variety of clever thoughts can be found, together with conventional ideas like professional platforms techniques and smooth computing innovations like fuzzy good judgment, neural networks, or genetic algorithms. those options are complementary methods to clever details processing instead of competing ones, and hence higher ends up in challenge fixing are completed while those concepts are mixed in hybrid clever platforms. Multi-Agent structures are perfect to version the manifold interactions one of several assorted parts of hybrid clever systems.This publication introduces agent-based hybrid clever platforms and offers a framework and method making an allowance for the advance of such platforms for real-world functions. The authors concentrate on functions in monetary funding making plans and information mining.
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Extra info for Agent-Based Hybrid Intelligent Systems: An Agent-Based Fromework for Complex Problem Solving
The antecedent sets are combined by means of operators that are analogous to the usual logical conjunctives “and,” “or,” etc. One method of storing and representing fuzzy rules is through the use of a fuzzy associative memory (FAM) matrix . FAM is a very simple and useful way to process fuzzy rules. 2 shows an example of a FAM matrix that represents the nine rules described in Sect. 1. For example, the shadowed entry in Fig. 2 represents rule 8 (refer to Sect. 1). 3 Neural Networks Neural networks, often referred to as artificial neural networks to distinguish them from biological neural networks, are modeled after the workings of the human brain.
Based on the three factors, they have divided hybrid systems into three classes: function-replacing, intercommunicating and polymorphic. Function-Replacing Hybrids Function-replacing hybrids address the functional composition of a single intelligent technique. In this hybrid class, a principal function of the given technique is replaced by another intelligent processing technique. The motivation for these hybrid systems is the technique enhancement factor discussed above. Intercommunicating Hybrids Inter-communicating hybrids are independent, self-contained, intelligent processing modules that exchange information and perform separate functions to generate solutions.
Genetic algorithms, especially in the form of classifier systems, can build reasoning models in the form of rules. As in the case of expert systems, it is possible to trace a chain of inference and provide some degree of explanation of the reasoning process. 3 Classification of Hybrid Intelligent Systems 23 In contrast, in neural networks it is difficult to provide adequate explanation facilities. This is due to neural networks not having explicit, declarative knowledge representation structures, but instead having knowledge encoded as weights distributed over the whole network.