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The objective of this study is to find a sequence of jobs for the permutation flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the 10 machine problem taken from the literature. The network is trained with the optimal sequences for five, six and seven jobs problem. This trained network is then used to solve the problem with greater number of jobs. The...
Neural net is one net structure used to simulate the structure of human brain, which has a number of realization methods such as the simulated annealing algorithm, the BP algorithm and genetic algorithm and so on. The simulated annealing algorithm is a kind of calculation precision of random search algorithm, which can be applied to many of little premise information questions and converged to the...
Based on the steepest descent method, back-propagation neural networks (BPNNs) minimize an energy function for errors occurring between desired and actual outputs. Therefore, conventional BPNNs obtain local optimum weights. Stochastic search optimization methods, such as genetic algorithms, particle swarm optimization methods and artificial immune system (AIS) algorithms, have been extensively used...
Genetic back propagation (BP) neural network is fast, quick, steady in forecasting of traffic flow, and the result has lowly error ability. But it can easily cause premature convergence, and usually the solution we got is local optimal solution. For overcoming those drawbacks of Genetic BP neural network, we add Simulated Annealing Algorithm to the processing of GA, using the ability of Annealing...
Since the BP neural network algorithm has some unavoidable disadvantages, such as slowly converging speed and easily running into local minimum, the genetic algorithm and simulated annealing algorithm with the overall search capability have been put forward to optimize authority value and threshold value of BP nerve network. In this paper, a new neural network model which is optimized by genetic algorithm...
The BP neural network algorithm can not guarantee an error plane as the overall minimum in the training process. It may have a number of local minimum rather than the optimal solution to the issue. To solve this issue, a new genetic algorithm of self-adaptive annealing is designed on the basis of standard genetic algorithm, combined with algorithms for global optimization of simulated annealing to...
Artificial neural network (ANN) has outstanding characteristics in machine learning, fault, tolerant, parallel reasoning and processing nonlinear problem abilities. But BP training algorithm is based on the error gradient descent mechanism that the weight inevitably fall into the local minimum points. In this paper, a hybrid genetic algorithms(HGA) was proposed to solve the problem. The proposed HGA...
Since the BP neural network algorithm has some unavoidable disadvantages, such as slowly converging speed and easily running into local infinitesimal, the genetic algorithm and simulated annealing algorithm with the overall search capability has been put forward to optimize authority value and threshold value of BP nerve network. In this paper, GA-SA neural network algorithm model has been established...
Since the BP neural network algorithm has some unavoidable disadvantages, such as slowly converging speed and easily running into local infinitesimal, the genetic algorithm and simulated annealing algorithm with the overall search capability has been put forward to optimize authority value and threshold value of BP nerve network. In this paper, GA-SA-BP neural network algorithm model has been established...
Lung cancer is a material cause of cancer death. To forecast CT diagnosis of lung cancer, this paper proposes a hybrid genetic algorithm-BP neural networks (GA-BP algorithm), which introduces multi-species co-evolution genetic algorithm (MCGA) and simulated annealing algorithm (SA), to solve the problem of traditional GA-BP algorithm and avoid trapping in a local minimum. Experiments indicate that...
The forecast of soil moisture is the basis of agriculture water-saving irrigation. This paper designs and implements a real-time prediction system of soil moisture based on GPRS and wireless sensor network. Front-end of system uses wireless sensor network to collect moisture data, GPRS network to transmit data; back-end uses genetic BP neural network to analyze and process data, simulated annealing...
Human capital formation and accelerating economic growth is a representative complex system which is not suitable to measure and forecast by classic linear statistical approaches. This paper presents an approach of fusing genetic algorithm (GA), simulated annealing (SA) and error back propagation neural networks (BPNNs) to predict human capital of China regions. Adopting multi-encoding, the GA-SA-BPNNs...
This paper investigates the problem of feature subset selection as part of a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. This technique combines both global and local search strategies for the simultaneous optimization of the number of connections and connection values of multi-layer perceptron neural networks. We compare the performance...
It is present herein an evaluation of the effect of different cost functions on a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. We investigated five cost function approaches: the average method, weighted average, weight-decay, multi- objective optimization, combined multi-objective and weight- decay. The weight-decay approach presented...
It is a key issue that constructing a successful knowledge base to satisfy an efficient adaptive scheduling for the complex manufacturing system. So a hybrid backpropagation (BP)-based scheduling knowledge acquisition algorithm is presented in this paper. We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid optimization method, in which GA was introduced to present...
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