A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems

Abstract

Linguistic modeling of complex irregular systems constitutes the heart of many control and decision making systems, and fuzzy logic represents one of the most effective algorithms to build such linguistic models. In this paper, a linguistic (qualitative) modeling approach is proposed. The approach combines the merits of the fuzzy logic theory, neural networks, and genetic algorithms (GA's). The proposed model is presented in a fuzzy-neural network (FNN) form which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The learning algorithm of an FNN is composed of three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed and used to extract the linguistic-fuzzy rules. In the third phase, a multiresolutional dynamic genetic algorithm (MRD-GA) is proposed and used for optimized tuning of membership functions of the proposed model. Two well-known benchmarks are used to evaluate the performance of the proposed modeling approach, and compare it with other modeling approaches.

System Identification Using Fuzzy Cerebellar Model Articulation Controllers

“…Training data and approximated data obtained using the P-FCMAC network for 300 epochs Table 3 shows the comparison the learning result among various models. The previous results were taken from (Wan & Li, 2003;Wang et al, 1995;Farag et al, 1998;Juang et al, 2000). The performance of the very compact fuzzy system obtained by the P-FCMAC network is better than all previous works.…”

“…Methods Error Methods Error P-FCMAC 0.00057 (Scheme 1) Gradient Descent (Wang et al, 1995) 0.2841 0.00024 (Scheme 2) SGA-SSCP (Wan & Li, 2003) 0.00028 MRDGA (Farag et al, 1998) 0.5221…”

Applications of Genetic Algorithm in Power System Control Centers

“…An example of this type of integration is when genetic algorithm training a neural network. A good example of this application in power system control is presented in (Farag et al, 1998, Farag et al, 1999. In this example, a genetic algorithm training system is used in a fuzzy-neural model shown in Figure 6.…”

“…6. Example of fuzzy-neural model training by genetic algorithms (Farag et al, 1998) The third type is named fused systems. In this type, the helped methodology and the genetic algorithms run completely together.…”

A genetic algorithm based recurrent fuzzy neural network for linear induction motor servo drive

“…For use in control engineering, GAs can be applied to a number of control methodologies for the improvement of overall system performance. In most controller designs, some parameters are required to be optimized in order to give better overall control performance (Moin et al, 1995;Lin and Chen, 1995;Oh et al, 1999;Kuo and Li, 1999;Farag et al, 1998;Cheng and Wong, 2002). Moin et al (1995) designed the static gain matrix for use in the reaching phase of a sliding-mode control system using GA. Lin and Chen (1995) applied GA to obtain the center of membership functions in a fuzzy sliding-mode controller.…”