Fuzzy Relation-Based Neural Networks and Their Hybrid Identification

In this paper, we develop a comprehensive identification scheme for fuzzy relation-based neural networks (FRNNs). The proposed hybrid development approach combines the optimization technology of genetic algorithms (GAs) and an improved complex method introduced in the previous studies on fuzzy modeling. The structure of the FRNNs revolves around a collection of fuzzy rules and involves two types of fuzzy inference schemes. The taxonomy of these schemes relates to the format of the conclusion part of these rules (being either constants or linear functions). The optimization of the network deals with a number of essential parameters as well as the underlying learning mechanisms (e.g., apexes of membership functions, learning rates, and momentum coefficients). The hybrid identification approach helps achieve global optimization (when using GAs) and assure local convergence (that results from the use of the improved complex method). During the identification process, we are guided by a weighted objective function (performance index) in which a weighting factor is introduced to achieve a sound balance between approximation and generalization capabilities of the resulting model. The proposed identification method is applied to nonlinear processes (data) such as gas furnace process data and emission process data form a gas turbine power plant. The obtained experimental results show that the proposed networks exhibit high accuracy and generalization capabilities.