A new fuzzy neural network with fast learning algorithm and guaranteed stability for manufacturing process control
In this paper, a new fuzzy neural network (FNN) is presented for manufacturing process control. It is different from the conventional FNN in its structure, learning algorithm and stability analysis method. Firstly, it utilizes the input and output layer to on-line fine-tune scaling factors. It can also use the hidden layers to realize the fuzzification, fuzzy inference, defuzzification and tune parameters such as membership functions, fuzzy control rules dynamically. Secondly, a new combining learning algorithm (CL) which combines the gradient-based error back-propagation algorithm (EBP) with similar Newton (SN) algorithm is proposed in order to improve the convergence speed and release computational burden during the learning process. Lastly, a convergence condition for determining the stability of FNN is established. Physical experiments for manufacturing process control are implemented to evaluate the effectiveness of the proposed scheme. (C) 2001 Elsevier Science B.V. All rights reserved.
learning stability analysis neuro-fuzzy systems lyapunov's direct method