Research On Fuzzy Neural Network Based On Improved Particle Swarm Optimization

Recently, fuzzy neural networks attract more and more attentions from academic circle. Fuzzy logic has the ability of mimicking human reasoning capabilities, and it is widely used in pattern identification, expert systems,fault diagnosis,system identification and in the control of nonlinear systems. Neural networks have a few advantages, such as adaptive learning, parallelism, fault tolerance, and generalization. Fuzzy neural networks combine the advantages of the above two approaches, overcoming the"black-box"nonlinear mapping from input to output, and also, the subjectivity of selecting fuzzy rules by human. It is predicted by many experts that fuzzy neural networks would become the core technique in the region of intelligent control in 21 century. The study algorithm of fuzzy neural network has being thought much of.The known study algorithms which are used to be Fuzzy Neural Network parameter study algorithms are BP algorithm with gradient descent and inheritance algorithm. However, BP network with gradient descent has some defects such as low convergence speed, fall in local minima. And inheritance algorithm has too many parameters to be set. So Particle Swarm Optimization has been introduced to train the weigh of Fuzzy Neural Network.PSO has the advantage of simple and easy to achieve and not many parameters need to be adjusted. Has been widely used in function optimization, training neural networks, fuzzy systems control, and other fields.For FNN weight training in non-linear, complex process, Linear Particle Swarm Optimization algorithm which makes the inertia weightωreduction linearly often fails to reflect the actual optimized search process. Dynamic particle swarm algorithm can be used to achieve the nonlinear search, but it is easy to fall into local optimization.So Tabu Search based Dynamic particle swarm algorithm was presented. The algorithm was introduced to settle local optimization of Dynamic particle swarm algorithm. And carried on a modification to Tabu Search's formula, make it can solve the problems both the minimum optimal and the maximum optimal. According to experiment result, improved algorithm has better result and faster optimal speed than Linear Particle Swarm Optimization and Dynamic particle swarm algorithm. And, in FNN weight training, improved PSO in the convergence rate and the ability to jump out to local optimum algorithm is better than BP.