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Aiming at manually carry through optimization of experiment way adopted for traditional PID controller parameter, an optimization method based on improved ant colony algorithm for PID parameters of BP neural network is presented. The improved ant colony algorithm and BP neural is organically combined by this method. Which not only overcomes effectively the shortcoming of BP algorithm on some degree...
A new model based on improved ant colony algorithm (ACA) and backpropagation (BP) is proposed to predict Silicon content of hot metal in blast furnace. BP algorithm has been widely used in training artificial neural network (ANN), which is an outstanding model to predict Silicon content. BP algorithm has many attractive features, such as adaptive learning, self- organism, and fault tolerant ability...
BP neural network has some shortcomings, such as low-precision solutions, slow search speed and easy convergence to the local minimum points. Ant colony algorithm(ACA) is a novel simulated evolutionary algorithm which accounts for rapid discovery of good solutions and easy to realize distributed computation. This paper establishes ant colony neural network model and applies in lithology recognition...
Introducing the rank-weight method into the basic ant colony optimization (ACO), we use the modified ACO to optimize the weights and thresholds value of neural networks (NN). And when the BPNN is being trained, this method can solve the disadvantages of running into the minimum easily, and enhance the convergence speed. So we get a heuristic method, which is good at time efficiency and derivation...
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