The standard back-propagation (bp) algorithm for multilayer feedforward neural networks is basically a gradient-descent method, it has the problems of local minima and slow convergence. In this paper, a simple method ...
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Based on rough set and basic theory of data fusion, the data fusion algorithm combining rough set theory and bp neural network is studied. Since rough set theory can effectively simplify information, cut down the tagg...
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Based on rough set and basic theory of data fusion, the data fusion algorithm combining rough set theory and bp neural network is studied. Since rough set theory can effectively simplify information, cut down the tagged dimension . This paper will be rough set theory and neural networks combined, using channel capacity of knowledge relative reduction algorithms to simplify the input information. Rough set theory is first used to process the sample data, and eliminate the redundant information, then reduce the scale of neural network, improve the identification rate, and improve the efficiency of the whole data fusion system. The effectiveness of the improved algorithm is demonstrated by an example compared with the traditional neural network system.
Financial time series are nonlinear,non-stationary and have a long memory,which make it difficult to achieve the desired prediction *** this paper,we propose a forecasting method,combining the wavelet analysis and the...
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Financial time series are nonlinear,non-stationary and have a long memory,which make it difficult to achieve the desired prediction *** this paper,we propose a forecasting method,combining the wavelet analysis and the particle swarm optimization(PSO) neural *** use the wavelet analysis to de-noise the non-stationary time series,and then employ bp neural network based on PSO to predict the time series after *** paper researches the selection criteria of a wavelet function,forecasts the time series of Shanghai Composite Index closing prices with wavelet analysis and compares the results with traditional prediction *** is obvious that after wavelet de-noising,the same neural network analysis increases the forecasting prediction accuracy by almost an order of magnitude and the prediction error is much smaller than the prediction error without wavelet *** method improves the hysteretic nature and can achieve the ideal *** facilitates the analysis of the characteristics of financial risks and financial markets.
By using alternating current plasma arc welding,the influences were studied of such parameters as welding curent,arc voltage,welding speed,wire feed rate,and magnitude of ion gas flow on front melting width,wdle rei...
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By using alternating current plasma arc welding,the influences were studied of such parameters as welding curent,arc voltage,welding speed,wire feed rate,and magnitude of ion gas flow on front melting width,wdle reinforcement,and back melting width of LF6 aluminum *** of the formation of welding seam in alternating current plasma arc welding of aluminum was set up with the method of artificial neural neural network - bp algorithm. Qyakuty of formation was consequently predicted and *** experimental result shows that,compared with other modeling methods,artificial network model can be used to more accurately predict formation of weld,and to guide the production practice.
This paper is about the application of ANN (artificial neural networks) theory in evaluation of mine design schemes and a quantified evaluation method based on a three\|layer neural network is given. It studies the st...
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This paper is about the application of ANN (artificial neural networks) theory in evaluation of mine design schemes and a quantified evaluation method based on a three\|layer neural network is given. It studies the structure of the three\|layer neural network, its learning process, its operating algorithm to realize the evaluation of mine design schemes in a computer and a practical example is also involved in it.
This paper provides a character recognition system based on the Back Propagation artificial neural network that is *** with samples of the characters,the characters are recognized,using with VC++.Recognition rate reac...
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This paper provides a character recognition system based on the Back Propagation artificial neural network that is *** with samples of the characters,the characters are recognized,using with VC++.Recognition rate reaches 96%,which meets the design requirement.
Artificial neural network is composed by a large number of processing interconnected unit. It is a nonlinear, adaptive information processing system. It has self-organizing, adaptive and self-learning ability. And can...
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Artificial neural network is composed by a large number of processing interconnected unit. It is a nonlinear, adaptive information processing system. It has self-organizing, adaptive and self-learning ability. And can be used to calculate complex relationship between input and output, thus it has effective control ability. Rubber is the main material in the process of the mixer. It has a great influence on the final product. But at present many factories are added in the rubber by manual control. So this article takes the rubber transport equipment as the object, establish a bp neural network intelligent control system, using neural network self-learning ability and the adaptive ability to deal with uncertain information, to solve the problem of motion control of multiple dynamic input. Through the test of practical application, the system has strong robustness, adaptability, good generality and fault tolerance.
For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection o...
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For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection of hot-rolling control parameters was studied for microalloy steel by following the neural network principle. An experimental scheme was first worked out for acquisition of sample data, in which a gleeble-1500 thermal simolator was used to obtain rolling temperature, strain, stain rate, and stress-strain curves. And consequently the aust enite grain sizes was obtained through microscopic observation. The experimental data was then processed through regression. By using the training network of bp algorithm, the mapping relationship between the hotrooling control parameters (rolling temperature, stain, and strain rate) and the microstructural paramete rs (austenite grain in size and flow stress) of microalloy steel was function appro ached for the establishment of a neural network-based model of the austeuite grain size and flow stress of microalloy steel. From the results of estimation made with the neural network based model, the hot-rolling control parameters can be effectively predicted.
In this paper,two kinds of methods, namely additional momentum method and self-adaptive learning rate adjustment method, are used to improve the bp algorithm. Considering the diversity of factors which affect stock pr...
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In this paper,two kinds of methods, namely additional momentum method and self-adaptive learning rate adjustment method, are used to improve the bp algorithm. Considering the diversity of factors which affect stock prices, Single-input and Multi-input Prediction Model (SIPM and MIPM) are established respectively to implement short-term forecasts for SDIC Electric Power (600886) shares and Bank of China (601988) shares in 2009. Experiments indicate that the improved bp model has superior performance to the basic bp model, and MIPM is also better than SIPM. However, the best performance is obtained by using MIPM and improved prediction model cohesively.
Big data has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of innovation in strategy and model in contemporary business organizations. In the paper, ...
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Big data has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of innovation in strategy and model in contemporary business organizations. In the paper, data mining perspectives are pointed out about how business model innovation is driven by "key data" based on illustrations about big data. Nine basic factors can be represented as business model innovation. GA-bp model is constructed by the combination of genetic algorithm and bp algorithm to extract the knowledge from the data in data mining environment and to find associations, patterns by analyzing the big data sets. Finally, "key data" that affects the consequence significantly can be grabbed to explore entry points for business model innovation in the era of big data, and to offer enterprises and executives for business model innovation from a new version.
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