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This paper presents the artificial neural network (ANN) that used to perform the short-term load forecasting (STLF). The input data of ANN is comprises of multiple lags of hourly peak load. Hence, imperative information regarding to the movement patterns of a time series can be obtained based on the multiple time lags of chronological hourly peak load. This may assist towards the improvement of ANN...
Traditional method of tender offer is subjective and arbitrary, and the ARIMA Accuracy can't satisfy the tenderer. We have combined the Elman NN with the SVM model to establish a new hybrid optimization algorithm, which are presented to the bidding tender offer in a project. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature...
This paper briefly analyses the conventional reactive power optimization compensation. The new method proposes a new optimization reactive power compensation for electrical network that uses neural network to predict electric network's important parameters and nonlinear prime-dual interior algorithm to optimize reactive power. This intelligent control system diminishes power losses, and settles the...
On the basis of traditional BP neural network model, a series of optimization methods are used to improve application capability and range in the water quality assessment, which can help diagnosis of River Basin Water Environment. The improved models are applied to evaluate water quality of Moshui river china, from 2001 to 2007, and relative conclusions are obtained.
Water quality model is a useful tool to evaluate the future state of river water through evaluation of actual pollution loading or different management options. Based on the human brain physiology research, artificial neural network(ANN), which simulates the structure and mechanism of the human brain, is a kind of dynamic information processing system that eventually achieves certain functions of...
A novel G-S chaotic neural networks is proposed based on G-S chaotic neuron, whose structure is similar to BP neural networks. The activation function is no monotonous function and the neurons have two states. In the process of learning, the states of neurons are chaotic. According to the states, the weights can be adjusted. In the process of working, the sates of neurons are not chaotic. The learning...
In this paper, the quality control of plastic gears manufacture, on which various factors are influential, is analyzed as a systematic engineering. Applying FEA techniques and optimization methods, with the study of plastic Gear forming process theory and realization of the process of numerical simulation, injection molding process parameters are optimized based on based on CAE, neural networks and...
Chaotic neural networks have been proved to be powerful tools for escaping from local minima. In this paper, we first review Chen's chaotic neural network and then propose a novel chaotic neural network model. Second, we make an analysis of the most positive Lyapunov exponent of the neural units of Chen's and the proposed model. Third, 10-city traveling salesman problem (TSP) is given to make a comparison...
The following topics are dealt with: adaptive control; observers; fault diagnosis; networked control systems; power systems; agent-based systems; control theory; control applications; genetic computation; evolutionary computation; robotics; nonlinear systems; electromechanical systems; neural networks; predictive control; robust control; discrete event systems; embedded control systems; process control;...
High-tech industry zone play a important role in the global economic system with the continuously new knowledge innovation, it's becoming the main motion of regional economic structure optimization and competition. The technology ability and diffuse effect of High-tech zone influence itself and regional economic development greatly. This paper design High-tech zone technology innovation ability appraisal...
Probabilistic Neural Networks (PNN) learn quickly from examples in one pass and asymptotically achieve the Bayes-optimal decision boundaries. The major disadvantage of PNN is that it requires one node or neuron for each training sample. Various clustering techniques have been proposed to reduce this requirement to one node per cluster center. Decision boundaries of clustering centers are approximation...
A novel neural network with chaotic property is proposed. The network is composed by different neurons. Some activation function is chaotic iterative function instead of the conventional Sigmoid function. In the process of learning, taking advantage of the randomicity property and ergodicity property of chaos, the generalization capability and the optimizing efficiency can be improved. The simulation...
This paper focuses on traffic flow forecasting which is an essential component in traffic control or route guidance system. A combination forecasting model called GM-GRNN based on GM(1, 1) and GRNN is built for short-term traffic flow time series. The basic theory and features of General Regression Neural Network (GRNN) and its advantages are introduced. The weight of combination model is determined...
The traditional Dam deformation predicting focused on the optimization of the model itself, while ignoring the optimization for impact factors. This paper introduced correlation coefficient into the judgment of the optimization for impact factors. According to the comparative analysis on the discrimination for different relativity judging, Finally, The Pearson correlation coefficient method is determined...
Imperialist Competitive Algorithm (ICA) is a novel optimization algorithm that inspired by socio-political process of imperialistic competition. ICA shown its excellent capability in diverse optimization tasks. In this paper, a new method for training an Artificial Neural Network using Chaotic Imperialist Competitive Algorithm is proposed. In Chaotic Imperialist Competitive Algorithm (CICA) the chaos...
Power-line network is not specifically designed for high frequency signal transmission. Some tests and documents verify that power-line channel takes on many adverse characteristics that are not conducive to high frequency signal transmission, and noise characteristics are important parameters to describe the nature of power-line communication channel interference. The paper studied noise classification...
Call center has been paid more and more attention, a method for predicting call center service grade with improved neural network algorithm was put forward according to the call center service quality management requirements in the enterprise. The optimization algorithm Levenberg-Marquardt was utilized to increase the convergence speed of BP neural network. And overcome the shortcomings of falling...
The following topics are dealt with: data security; pattern classification; mobile computing; text-to-speech synthesis; user interfaces; Web services; medical computing; customer relationship management; optimisation; genetic algorithms; learning; data mining; computer aided instruction; software engineering; watermarking schemes; wireless sensor networks; educational administration; evolutionary...
A methodological proposal to estimate a Tailored to the Problem Specificity mathematical transformation is developed. To begin, Linear Analysis is briefly visited because of its significant role providing a unified vision of mathematical transformations. Thereafter it is explored the possibilities of extending this approach when basis of vector spaces are built tailored to the specific knowledge on...
In this paper a new tool is proposed as a possible aid to study differences and similarities between the human and the artificial neural network (NN) learning of some verbal and mathematical elementary abilities. For this purpose, simple NNs of the multi layer kind (MLNN) have been build. These MLNNs are able to recognize some graphemes and/or to make additions of integers up to 1000. An algorithm...
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