In this paper, based on the combination of Genetic algorithm and bp algorithm, a new algorithm is proposed in this paper. The bp operator is embedded in the genetic operation in the algorithm, the algorithm effectivel...
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In this paper, based on the combination of Genetic algorithm and bp algorithm, a new algorithm is proposed in this paper. The bp operator is embedded in the genetic operation in the algorithm, the algorithm effectively assimilates the global optimization of genetic algorithm and fast convergence of bp algorithm, and it encodes the construction and the weights hybrid with real code and binary code, achieving the same step optimization of structure and weights. The simulation results show that, the new algorithm can quickly converge to the global optimal solution, but also can obtain the best approximation of weights in the network structure.
An improved bp algorithm for pattern recognition is proposed in this *** the difference between the real output and the desired output is transformed with a function,the training error in the improved algorithm is mor...
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An improved bp algorithm for pattern recognition is proposed in this *** the difference between the real output and the desired output is transformed with a function,the training error in the improved algorithm is more consistent with the misclassification *** results show that the new algorithm works well in pattern recognition field and it converges much faster than conventional bp algorithm.
Aiming at bp algorithm convergence slow and be prone to plunge a partial basis,an improved bp algorithm——ACbp algorithm is proposed in this paper,it has better diversity and global search *** ability of optimization...
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Aiming at bp algorithm convergence slow and be prone to plunge a partial basis,an improved bp algorithm——ACbp algorithm is proposed in this paper,it has better diversity and global search *** ability of optimization for the algorithm is tested through numerical computation,the experimental demonstrates that the improved bp algorithm has better diversity and global search capacity than genetic algorithm,bp algorithm,ant colony algorithm and simulated anneal algorithm.
In this paper, a new L-M optimized bp algorithm combining Levenbery-Marquardt optimized algorithm and neural network model is successfully developed after analyzing the deficiency of the traditional B
In this paper, a new L-M optimized bp algorithm combining Levenbery-Marquardt optimized algorithm and neural network model is successfully developed after analyzing the deficiency of the traditional B
bp neural network model has been applied broadly. The output of bp model is always supposed into a certain range, such as [0,1]. However, the output usually doesn’t satisfy this condition in practical
bp neural network model has been applied broadly. The output of bp model is always supposed into a certain range, such as [0,1]. However, the output usually doesn’t satisfy this condition in practical
The occurrence of coal mine disaster related with many environmental and social factors. The relationship between them was uncertainty, and was a kind of coupling relationship, and was nonlinear. It was difficult to f...
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ISBN:
(纸本)9781612848334
The occurrence of coal mine disaster related with many environmental and social factors. The relationship between them was uncertainty, and was a kind of coupling relationship, and was nonlinear. It was difficult to fit the relationship between them using a mathematical model. This also was the important reason that coal mine disaster was always hard to predict. The artificial neural network based on bp algorithm had highly nonlinear mapping function. It could nonlinear map the relationship between the probability of coal mine disaster's occurrence and its effect factors on the condition of building no complex mathematical model. And then the probability of coal mine disaster could be predicted relatively accurately. It provided technical support for prevention and management of coal mine disaster.
Economic early warning is the recognition and judgment of the state of economic operation, and its research results directly affect the rational formulation of macro-control policies. However, the traditional early wa...
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Economic early warning is the recognition and judgment of the state of economic operation, and its research results directly affect the rational formulation of macro-control policies. However, the traditional early warning methods are mainly based on expert experience or simple statistical model, which are difficult to reflect the nature of highly nonlinear economic system and can not meet the objective requirements of macroeconomic early warning. Based on the above background, the purpose of this study is to design an economic early warning system based on improved genetic and bp hybrid algorithm and neural network. Based on the overview of macroeconomic early warning at home and abroad, this study expounds the design of early warning index system, early warning model, establishment of early warning system and other issues in the macroeconomic early warning theoretical system;deeply analyses the theoretical methods of bp neural network and adaptive mutation genetic algorithm, and discusses the feasibility of realizing macroeconomic early warning by bp neural network and adaptive mutation genetic algorithm, The improved genetic and bp hybrid algorithm and neural network economic early warning model are established. Finally, the experimental results show that the correlation coefficient between the composite index and the comprehensive early warning is 0.89, and the delay number is 0, which shows that the early warning index obtained by the early warning system can accurately reflect the actual economic fluctuations. The results show that the improved genetic and bp hybrid algorithm and neural network economic early warning system are effective, feasible and have good accuracy.
With the fast development of mobile ad hoc networks (MANETs), fault diagnosis has become a critical need to guarantee robust service for various applications. Many techniques have been suggested to solve this problem,...
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With the fast development of mobile ad hoc networks (MANETs), fault diagnosis has become a critical need to guarantee robust service for various applications. Many techniques have been suggested to solve this problem, but they still cannot satisfy the special need of MANETs. In this paper, we propose a new fault diagnosis system using hybrid GA-bp neural network. The results of simulation demonstrate that the performance of this system is excellent.
This The pellet sintering process is a physical chemistry process,which is pure time-delay and nonlinear with more variables and discrete *** order to solve this problem,a new bp algorithm of NN for the temperature id...
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This The pellet sintering process is a physical chemistry process,which is pure time-delay and nonlinear with more variables and discrete *** order to solve this problem,a new bp algorithm of NN for the temperature identification of sintering shaft furnace is introduced in this *** network is combined with PID control and simulation effects are given with *** experiment results show this method can make the temperature of firebox conform the requirement of the control standard and can also improve the output and quality of the products.
The Error Back Propagation is iterative, the way of implementing it using the iterative Map Reduce framework is presented. To solve the shortage of the traditional Map Reduce framework in iterative program, the iterat...
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ISBN:
(纸本)9781510821965
The Error Back Propagation is iterative, the way of implementing it using the iterative Map Reduce framework is presented. To solve the shortage of the traditional Map Reduce framework in iterative program, the iterative Map Reduce framework has added a transmitting module. Through the simulation of the control system in the K/TGR radioswitch to get the training sample. Training the sample based on the traditional framework and the iterative framework on Hadoop. The experimental results show that the bp algorithm based on the iterative framework demonstrated faster speed and higher accuracy. The iterative framework can effectively reduce the training time and avoid the problem in iterative calculation.
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