Intelligent Dynamic Acquisition Learning Fuzzy Modeling Systems Design

Abstract

Technologies of fuzzy knowledge-based discovery can be exploited to extract appropriate behaviors for identified modeling problems. A proposed novel data acquisition learning algorithm (DALA) is dynamically applied to examine the initial recognitions of the training data set, where it can automatically establish the number of cluster centers and their associated positions. The characteristics of the training data set are dynamically mined by the DALA, and the primary architecture of the fuzzy system is initially represented with the collected information. Because the prototypes of the collected training data are determined with DALA, the initial populations of the particle swarm optimization (PSO) are generated and distributed around them. The hybrid PSO and recursive least-squares (RLS) learning schemes referred to as RPSO are applied to design an appropriate fuzzy modeling system. The approximation of the proposed fuzzy modeling system shows that it automatically determines suitable fuzzy membership functions and resolves the local optimal problem of identifying appropriate parameters for fuzzy systems to swiftly approach desired functions. A comparison between computer simulations and other modeling methods shows the excellent performance of the dynamic acquisition learning fuzzy modeling system in explaining nonlinear and inverted pendulum function approximations. Simulation results show that the generated fuzzy system with the DALA scheme can be adapted to balance the pole position for various initial conditions. The proposed control rules swiftly recover the sudden and large imported noise.