Research On Neural Network Based On Ant Colony Optimization Algorithm And The Application

Multi-layer feed-forward neural network is the most widely used network, but it still has some difficulties in theory and applications, such as the local minimum, the learning and generalization ability. Ant Colony Optimization (ACO) algorithm is a typical intelligent algorithm that has good global optimization and parallelism. Therefore, using the ACO algorithm to train the weight value and threshold value of network can make it possible to escape from the local minimum, and improve its generalization capability.With the development of communication technology and people's increasing demand for bandwidth, the microstrip antenna's bandwidth technology and miniaturization have become a hot topic in the current study. Micorsrtip antennas have been used in many fields because of its advantages, such as lightweight, thin profile, packaging, installing, low-cost and so on. Therefore, it is a significant task to research high performance micorsrtip antennas.In this thesis, the basic concepts of neural network and ACO algorithm will be introduced firstly, and then the parameters selection and updating methods of ACO algorithm are studied. Three network models based on ACO algorithm or ACO algorithm connect with other algorithms are constructed (called ACONN in this thesis).The performance of these three models are verified through function fitting, LED classification and general XOR problem. At last use the ACONN model which has the best performance to calculate the resonant frequency of microsrtip antenna and also to design and optimization of an I-shaped microsrtip antenna.The main research results in this thesis can be summarized as follows:(1) To overcome the local minimum of ACO algorithm, this paper adopted generation-difference, chaos and variation of particles, and other updating algorithms. Many stimulation experiments are made. The experimental results show that the methods may effectively overcome premature of ACO algorithm, speed up the convergent rate, escape from local minimum, and approach to global optimum.(2) Three network models are constructed which are called ACONN in this thesis. The ACO algorithm can make the network escape from local minimum limitation, and improve its generalization capability further. ACO_NN is a model which neural network's weight value and threshold value are trained by ACO algorithm. ACO_BP_NN is a model which neural network's weight value and threshold value are trained firstly by ACO algorithm and then trained by BP algorithm. PSO_ACO_NN is a model which firstly adjusted ACO algorithm's initial value by Particle Swarm Optimization (PSO) algorithm, then trained neural network's weight value and threshold value by ACO algorithm.(3) To verify the performance of three ACONN models through function fitting, LED classification and general XOR problem. The computing results show that PSO_ACO_NN model has the best effects in dealing with these issues.(4) PSO_ACO_NN model is applied to calculate the resonant frequency of micorsrtip antenna. PSO_ACO_NN model is also used to optimize and design the structure of an I–shaped microstrip patch antenna. The simulating results on the two problems are satisfactory.