Study On The Optimization Of Artificial Neural Network Based On Intelligent Algorithm And Its Application

Along with wider application of artificial neural network, the optimization algorithm and the application technique of it have become the most important research orientation in the field. At present, certain progress has been made in the fields of neural network optimization using intelligent algorithm and fuzzy logic control using neural network, but there still exists the limitation of geometrically growing training data and the unstable performances. Based on sufficient research in the basic conceptions of neural network, colony intelligent algorithm and fuzzy logic, the optimization of the structure design and learning rule of neural network by using novel colony intelligent algorithm, the realization of supervised fuzzy clustering and adaptive neural-fuzzy control by using optimized neural network, have been discussed in the thesis.1. A training algorithm of neural network based on QPSO is proposed, which train the neural network by means of the quantum particle swarm optimization. The algorithm show better convergent speed and better global convergent characteristic when dealing with more dispersed issue compared with the traditional neural network training algorithm.2. A novel algorithm of neural network structure design based on BQPSO and 2-dimension cellular automate system is proposed. The algorithm introduces unique indirect encoding schema representing the structure of neural network, using the cell in the 2-dimension cellular automate system representing the existence of connection in neural network, by separately evolving the coordinates and value of the cell, the growing and pruning of the network structure is achieved. Create and evolve the coordinates of the cell by virtue of BQPSO with specially-designed fitness function, evolve the value of the cell using properly-designed neighboring evolving rule of cellular system, train current network with float-point QPSO, the final stable structure is found step by step.By separately evolving the coordinates and value of the cell, the proposed algorithm can solve the problem of geometrically growing encoding length and the difficult realization of commonly used structure design algorithm.3. The research of the supervised fuzzy C-clustering neural network. A novel objective function of fuzzy clustering that integrates the clustering characteristic of input space and the real-time approximate characteristic of output space is proposed, thus importing the supervise factor into the former fuzzy clustering. An extraordinary neural network handling the SFCM is also proposed. SFCM has better performance in the stable convergent rate, convergent speed, as well as in the initial condition sensitivity compared with traditional fuzzy clustering.4. The research of the neural-fuzzy control. According to the issue of dynamic path plan of mobile robot in unknown environments from the start to the destination with obstacle avoidance, a systemic neural-fuzzy control algorithm is proposed. Fuzzy logic control is designed to do the input fuzzification, fuzzy reasoning rule base, output defuzzification. The simplified structure of neural network handling the fuzzy control based on the rule base and the corresponding simplified network parameter set is also designed. Train the network using QPSO. Solve the "dead cycle" problem in U-shaped obstacle through the storage and management strategy of state variable of robot.The algorithm solve the problem of big network scale, low network performance via using grade-descend training method in the conventional neural-fuzzy system, and the problem of getting into the dead cycle when striding across the U-shaped obstacle is also solved.