Optimization of Rule Design Parameters

You can use the Takagi-Sugeno fuzzy nomenclature system (T-S FCS) using particle cluster optimization (PSO) and the support vector machine (SVM) for parameter optimization. T-S FCS is synthesized by a fuzzy IF-THEN rule whose result is a linear equation of state. The guardian of T-S FCS is not shaken by the fuzzy membership of the input full-length vector. During the source configuration process, predefined values are optimized remotely using PSO. The resulting parameters of T-S FCS are learned through SVM. The proposed T-S FCS is worldly in minimizing the impact of uncertainty, reducing the influence of transforming factors, and repaying the system maximum generalization performance inheriting the advantages of T-S fuzzy systems, PSO, and SVM.

It is very important to set the initial values of the parameters, which directly reduces the quality of the algorithm. So, during the source configuration process, the values pre-specified are optimized remotely using PSO. PSO is an evolutionary computational technique and is similar to genetic algorithms (GA) in that the system is initialized with a set of random solutions. However, it differs from GA in that each potential solution is defined at a random rate. Thus, PSO outperforms GA in terms of search processing time and convergence speed.


In general, fuzzy tenancy systems are still the most important applications of fuzzy theory. This is a generalized form of expert tenancy that uses fuzzy sets with fuzzy rules in system modeling. An important idea in Zadeh's and Mamdani's classic fuzzy approaches is to summarize the conclusions by evaluating the concordance layer from observations that triggered one or several rules in the model. In most fuzzy modeling or fuzzy tenancy systems, experiments and simulations are set up to produce datasets that more weightily describe all possible outcomes. Without this, human experts create a set of more weighted fuzzy rules that do tenancy or modeling. In general, fuzzy rules created in this way weaken the entire universe, taking into account all possibilities. However, serious problems can arise due to the high computational time and spatial complexity of the rule base used to describe models with multiple input variables with adequate accuracy. "Exponential explosion" seldom allows unqualified systems that use classic fuzzy algorithms that increase the number of input variables or use them in real time.

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