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Field-programmable gate arrays (FPGAs) can provide an efficient programmable resource for implementing hardware-based spiking neural networks (SNN). In this paper we present a hardware-software design that makes it possible to simulate large-scale (2 million neurons) biologically plausible SNNs on an FPGA-based system. We have chosen three SNN models from the various models available in the literature,...
In order to study the impact of drivers' distance cognition difference on traffic safety in dynamic environment of daytime and night-time, a real road tests was carried out by asking 19 drivers randomly selected to percept the distances of obstacles with different distances and velocities on daytime and nighttime. The values of cognition are obtained by statistical methods. The distance cognition...
Artificial neural network is an important research direction in data mining. It is used to solve classification and regression problems, and can find out the nonlinear relation between the input attribute and the output attribute, especially the smooth and continuous nonlinear relations. Use the Microsoft neural network to find out how the meteorological factors influence the precipitation, and to...
The paper studies the application of principal component analysis and ANN (Artificial Neural Networks) for pre-warning of enterprise financial crisis, analyzes the factors of financial crisis, and constructs the model of the enterprise financial crisis with principal component analysis and ANN. It integrates simplifying of enterprise financial crisis index, dynamic learning of financial crisis knowledge...
This paper takes commercial bank as the object of study, conducts the research on its logistics finance service pattern's risk evaluation. First, the paper analyses the principal patterns of commercial bank logistics finance services, unifies the characters of each kind of logistics finance service pattern, constructs risk evaluation index of commercial bank logistics finance under different patterns...
In this paper, based on investigating and analyzing on the affecting factors for support type of development roadways as well as the successful support cases in Chengchao Iron mine, the improved BP neural network is put forward to study on the support type of development roadways. It may be seen from the learning course of learning samples and the prediction results of support types that whether the...
This thesis introduces the forecasting methods of domestic and foreign road traffic flow, analyzes the advantages and shortcomings of all sorts of traffic flow forecasting methods and the actual forecasting effects. For the complexity of the urban traffic, the precision of some current traffic flow forecasting methods is not high. With respect to these questions, this thesis applies the chaotic neural...
This paper proposed an unconstrainted optimization method based on BP neural network, and take Unipolar Sigmoid function as the transfer function,and take maximize network output as example,derived and given the partial derivatives of the BP neural network's output to input.On this basis, give a general mathematical expression that based on the BP neural network unconstrainted optimization issues,and...
Gas filow-volume controlled by many factors, the trend is complex, the accurate mathematical model to predict, in view of this situation, the paper attempts to grey dynamic model based on artificial neural network, organic combination of intelligent analysis method, structural gray neural network combination forecast model, based on Visual Basic 6.0, meanwhile, corresponding calculation program is...
According to the Characteristics of complex object system, a comprehensive evaluation model for complex object system is established based on fuzzy theory and artificial neural network. To realize the intelligence and the visualization of the evaluation process, an intelligent comprehensive evaluation software system with the help of Visual Basic, database technique and MATLAB toolbox is designed...
This article directs at coal manufacturing cost factors applying support vector machine (SVM) theory, it establishes forecasting model of coal manufacturing cost using wavelet neural network after reducting attribute of influence factors, in order to forecast the coal manufacturing cost effectively.
A novel neural network, retina neural network (RNN) is put forward, and its definition, network structure, parameters, and characteristic are presented. After training it using a set of samples, an ideal training waveform is obtained, which is a great guide for future research. Moreover, a model of permanent magnet synchronous motor (PMSM) PI controller is constructed based on RNN to test drive performances...
Short-term prediction of intelligent traffic flow is in favor of road unblocked and vehicle waiting strategy. The algorithm of short-term prediction of intelligent traffic flow based on back propagation(BP) neural network and autoregressive integrated moving average (ARIMA) model can solve it partially. Firstly, Establishing a BP neural network sub-model and ARIMA sub-model, Then taking BP neural...
Aiming at the shortages of the existing data-mining model for forecasting the industry security, a classification model based on rough sets and BP neural network (BPNN) is put forward in this paper. First, the theory of rough set is applied to pick up and reduce the index attributes. Then, the training samples are sent to the BPNN to train and learn. After that, the sorts of the coal industry security...
The crude oil demand is growing rapidly in China, driven by its rapid industrialization and motorization. China has already become the second-largest oil importer nation in the world, after the United States. The dynamic GM(1,1) model of grey theory is used to develop the dynamic GM(M,N) model to forecast the crude oil consumption and production in China. In order to improve the forecasting accuracy,...
In order to satisfy the needs of human resources management and development, this study took R&D professionals as the research object and proposed an evaluation model for high-tech enterprise human resources based on artificial neural network, then trained and tested the neutral network for personnel evaluation. And the network was improved to be very effective to stimulate the evaluation of human...
With financial globalization, the rapid development of financial derivatives and the complexity of banks management, operational risk measurement and management in commercial bank management is becoming increasingly important. How to effectively predict, control and prevent operational risk in commercial banks have become an important issue. Using BP neural network model to predict the risk has its...
On the basis of controlled nuclear fusion equipment HT-7 superconductive tokamak's detection data, this paper reports on an approach of nuclear fusion magneto hydrodynamics(MHD) pattern recognition by using artificial neural network and back-propagation(BP) neural network with delta-bar-delta rules which can monitor the system characteristics and recognize the MHD pattern precisely. The HT-7 nuclear...
The Hind marsh-Rose (HR) Neuron not only contained the features of cell biological physical model, but also included the traits of non-linear dynamical system model. The paper analyzed the HR model by computing manifold. It computed the equilibria, simulated the bifurcation phenomenon of the parameters, estimated the unstable local manifold and computed the global manifold using the angle constraint...
In this paper, two modeling approaches (artificial neural network and regression model) are established and used to predict the fiber diameter of melt blowing nonwovens. By analyzing the results of the models, the effects of process parameters on fiber diameter can be predicted. The results demonstrated that the ANN model yields more accurate and stable predictions than regression model, which is...
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