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In order to design the products that meet consumer emotional demands, this paper proposes a systematic method which combines neural network with genetic algorithm. Firstly, a back propagation neural network is applied to map the relationships between product design elements and customer kansei image evaluation. Secondly, generic algorithm is employed to search for the optimal product form which satisfies...
This article provides an overview of the application of certain soft computing tools namely, genetic algorithms (GAs), simulated annealing (SA), and artificial neural networks (ANNs) in certain tasks of RNA secondary structure prediction. Different tasks like prediction of helix, bulge, hairpin curve, internal loop, and multiloop are, first of all, described along with their basic features. The relevance...
Although simple genetic algorithm (SGA) can, to some extent, improve the back propagation neural network (BP), it is prone to prematurity and losing the optimal solutions. Niche technology and fuzzy control theory are introduced to improve SGA and the improved one is used to optimize BP. The improved genetic algorithm is used to optimize BP neural network. In addition, due to the increasingly voltage...
Baltic Capesize Index is very important for market operators to grip the change of Dry Bulk Shipping Market. This paper is trying to build a forecasting model for BCI (Baltic Capesize Index) based on ANN (Artificial Neural Network) optimized by GA (Genetic Algorithm). The result shows that the model can excellently extract the trend of BCI, and reflect changes in actual value with less error.
When exploring identification of coal and waste rock, 17 characteristic parameters of gray-scale histogram and gray level co-occurrence matrix (GLCM) were chosen according to their differences in gray scale and texture. Then, the principal component analysis (PCA) algorithm was used to get principal components from all the parameters chosen above. The principal components were defined as the inputs...
Component proportion sometimes is necessary in order to evaluate the state of research objects quantitatively or to do some specific studies. Back propagation neural network (BPNN) is one of mostly used neural networks because of its ability to achieve a high precision simulation for data problem or target function which is hardly to be established by conventional mathematical theory. In this paper,...
In order to overcome the shortcomings of BP neural network, the golden section theory was used to get the reasonable number of Back Propagation (BP) neural network's hidden nodes. By using Genetic Algorithm (GA) to optimize the initial weights and threshold value of BP neural network, the network converged quickly and the recognition precision was increased. The GA-BP neural network model was utilized...
Two different Approaches are used to Optimize Lemon Grass Oil Production. Oil Production is compared and Production-Nutrient ratio comparison also shows that Logistic function is giving best result for production-nutrient ratio. So logistic Function proved to be better for optimizing results in our case.
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