Application Research based on Artificial Fish-swarm Neural Network in Sintering Process
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Sinter tumbler strength is an important parameter in the sintering process, and has an important influence on the performance of finished sinter. Artificial fish swarm algorithm have good ability to acquire the global performance, the neural network has strong nonlinear ability and local optimization performance,; AFSA+BP algorithm combined with artificial fish swarm algorithm and BP algorithm, realizes the complementary artificial fish swarm algorithm global search capability and BP algorithm's local optimization combination of performance, an artificial fish swarm neural results show that the network combination algorithm, it is shown that comparing with the traditional BP neural network forecasting method，the presented forecasting method has better adaptive ability and can give better forecasting results.The artificial fish—swarm algorithm network is trained and checked with the actual production data．this algorithm has strong generalization capability, predictive accuracy improved significantly, and speed up the convergence rate, provides an effective method for strength prediction. Which be used for off-line learning and prediction, a good basis for the online application.In view of the shortcomings of AFSA(artificial fish swarm algorithm),the self-adjustment strategy on visual range and step size is introduced,so as to increase search efficiency and convergence rate.The improved AFSA may synchronously determine the parameters initiation value and hidden layer nodes number in search space.The simulation is given to illustrate the effectiveness of the method.
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Prediction of energy requirement is an important research topic. For fulfilling such prediction, neural network (NN) has testified to be a cost-effective technique superior to traditional statistical methods. But their training usually with back-propagation (BP) algorithm or other gradient algorithms, and some problems are frequently encountered in the use of these algorithms. In this paper, particle swarm optimization (PSO) is proposed to train artificial neural networks (ANN), and as a result, a PSO-based neural network approach is presented. The approach is demonstrated by predicting energy requirement in Xipsilaan city in China. The results show that the proposed approach can effectively improve convergence speed and generalization ability of NN.
Backpropagation
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For a nonlinear process of ship course motion subjected to random disturbances, it is difficult to predict. A new approach based on particle swarm optimization and back-propagation neural network algorithm was proposed to predict ship course. Combined particle swarm optimization with back-propagation neural network, this method utilized easy to realize, fast convergence speed and high accuracy merit of particle swarm algorithm to optimize the structure of neural network, and solve the problem in BP neural network which is sensitive with the initial weights, easy to fall into the local least value. The PSO-BPNN course prediction model of ship motion was established, then the model of PSO-BP neural network and BP neural network to predict ship course separately. Simulation results demonstrated that the proposed algorithm had a faster convergence rate and higher accuracy than prediction method of BPNN.
Backpropagation
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Artificial Fish-swarm Algorithm(AFSA) is a novel optimizing method proposed lately.An Artificial Fish-swarm Algorithm(AFSA) for the optimization of feed-forward neural networks and a model based on this method were presented for the first time here.Compared with the Back-propagation Algorithm added momentum,the Evolve Algorithm and the Simulated Anncaling Algorithm,optimization result of feed-forward neural networks by AFSA demonstrates that AFSA has a strong robustness and good global astringency.AFSA is also proved to be insensitive to initial values.
Robustness
Optimization algorithm
Backpropagation
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Backpropagation
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In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fish to train network weights parameters of PID neural network. By comparing food consistence in preying behavior to adaptively select vision and step of artificial fish, this method overcomes shortcomings such as slow convergence speed, low optimization accuracy of basic artificial fish algorithm. Simulations of PID neural network system whose parameters are trained respectively through BP algorithm and improved artificial fish algorithm are conducted respectively in the MATLAB environment. The simulation result shows that the PID neural network control system whose parameters are trained by the improved artificial fish algorithm has a better control effect, especially for nonlinear systems
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Artificial fish-swarm algorithm is a realization model of the swarm intelligence optimization algorithm.It uses the optimization model of imitated nature fish for feeding from top to bottom, clusters and rear, local optimization by individual fish, achieve the purpose of global optimal values highlighted in the groups.RBFNN based on the AFSA can accurately find the optimal solution quickly and ensure the diversity of artificial fish.It is easier to find the global optimal point of optimal fish.This design uses second-order pendulum as a controlled object, using artificial fish swarm algorithm applied to the neural network training algorithms, building design of RBF Neural networks control module , verifing by Matlab simulation of actual control controller performance.
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The BP neural network as the traditional prediction method has certain advantages, but it has some drawbacks, Such as slow convergence and sensitive to the initial weights, etc. The PSO algorithm is introduced into the neural network training, using the particle swarm algorithm to optimize the neural network weights and threshold. Through the establishment of the particle swarm - BP neural network model for power load budget, it improves the accuracy and stability of the forecast.
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In order to enhance global search capability and accelerate convergence rate of artificial fish swarm algorithm(AFSA),a new method is presented in which AFSA is combined with immune algorithm(IA),thus forming immune artificial fish swarm algorithm(IAFSA).The IAFSA is used to choose radial basis function(RBF) neural network's input variables automatically and train the weights that between network's hidden layer and output layer,which reduce RBF neural network's workload and improve it's training speed.Using the optimized RBF neural network to do short-term load forecasting,the results show that the proposed method has higher forecasting precision.
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According to the inherent defects of BP learning algorithm,we proposed an improved particle swarm neural network algorithm to improve the prediction ability of the BP neural network.We optimal the combination weights of BP neural network model parameters with the improved particle swarm optimization algorithm,then use BP algorithm to get the further accurate optimization of network parameters.The optimal parameter combination can be used to make prediction.Experimental results demonstrate the efficacy of the algorithm in the stock index prediction.
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