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The structures approximation analysis technology is studied based on neural network. The back-propagation neural network model corresponding to the size parameters of the hydraulic press' crossbeam and its displacement or stress is generated to replace the original finite element model in this paper. Using the saturated multi-level table of orthogonal arrays to choose the trained samples could make...
This The pellet sintering process is a physical chemistry process, which is pure time-delay and non-linear with more variables and discrete parameters. In order to solve this problem, a new BP algorithm of NN for the temperature identification of sintering shaft furnace is introduced in this paper. BP network is combined with PID control and simulation effects are given with MATLAB. The experiment...
In the human-machine-environment system of shooter-missile-battlefield, shooter is very important for image guided missile to track and attack targets in complex ground successfully. To build the model of shooter, the angle error between missile's optical axis and line of sight, and the change rate of the angle error were set to be inputs of model. The variable describing handle movement controlled...
A novel video smoke recognition method based on optical flow is presented. The result of optical flow is assumed to be an approximation of motion field. The method is proposed as following, first, moving pixels and regions in the video are determined by a background estimation method. Then, a pyramidal implementation of the Lucas Kanade feature tracker is proposed to calculate the optical flow of...
BP neutral network is a method to fuse datas from several dissimilar sensors for more precise results, but its low convergence rate and easily falling into local minimum often decrease the fusion precision. In order to overcome this drawback, we combined BP neutral network with genetic algorithm, and optimized connection weights and threshold value of BP neutral network by genetic algorithm. Then...
A turning control strategy based on BP neutral network and modified by a PID algorithm is presented in this paper for the dual electric tracked vehicle. Firstly analyzed the tracked vehicle turning dynamics and establish the torque distribution strategy. The BP network is trained by data from the distribution strategy simulation results; the PID algorithm is used to eliminate the dependence of terra...
Based on the static Preisach hysteresis model of Piezoelectric Smart Materials, the technology of Artificial Neural Networks is applied for the hysteresis modeling of Piezoelectric Smart Materials. The BP network is chosen as the method of identifying the non-linear hysteresis sysytem. Adopting this method one sample's hysteresis model is built. The results have shown that this method is correct and...
Because sugarcane average unit yield was affected by multiple factors in its growth and its inherent law was lack of external correlation data mining, the precise of the prediction method was low. Recently, the adaptive of modern intelligent genetic neural network algorithm for multi-factor effect has been strong, and the prediction accuracy has been high, but with which in sugarcane average unit...
A fast OBS pruning algorithm based on pseudo-entropy of weights is proposed to resolve the problems of the number of hidden neurons is difficult to be determined in neural networks and low pruning speed in conventional OBS (Optimal Brain Surgeon) pruning algorithm. The algorithm makes the network constrain the distribution of weight automatically during the training process, obtain a simpler structure...
In order to solve the problems of difficulty to determine the number of partitions and rule redundancy in neuro-fuzzy system modeling, this paper presents a new approach based on DENCLUE using a dynamic threshold and similar rules merging (DDTSRM). By introducing DDT, which uses a dynamic threshold rather than a global one in merging density-attractors in DENCLUE, our approach is good at determining...
Time series forecasting is an important aspect of dynamic data analysis and processing, in science, economics, engineering and many other applications there exists using the historical data to predict the problem of the future, and is one considerable practical value of applied research. Time series forecasting is an interdisciplinary study field, this paper is under the guidance of the introduction...
Analysing vibration signal is an effective important method for diesel engine fault diagnosis, and its key techniques are feature extraction and pattern recognition. In this paper, wavelet packet decomposition algorithm as an effective method for fault feature extraction is used to decompose the vibration signals, and its percentage of energy band wavelet packet and wavelet packet energy spectrum...
Arable land has been decreasing due to rapid population growth and economic development as well as urban expansion. To obtain a better understanding of controlling land use and to design mechanisms to ensure sustainable land management, an accurate prediction of arable land is a key issue fundamentally. In this study, artificial neural network (ANN) model is applied to estimate the arable land change...
Exchange rate time series is often characterized as chaotic in nature. The prediction using conventional statistical techniques and neural network with back propagation algorithm, which is most widely applied, do not give reliable prediction results. Exchange-rate time series is also a dynamic non-linear system, whose characteristics cannot be reflected by the static neutral network. The Nonlinear...
A water quantities allocation arithmetic was proposed, Radial basis function neural network (RBFNN) was designed, and simulated annealing arithmetic was adopted to adjust the network weights. MATLAB program was compiled; experiments on related data have been done employing the program. All experiments have shown that the arithmetic can efficiently approach the surface with 10-4 mm error precision,...
In order to avoid the economic loss due to too much or too little of electricity consumption, electricity consumption needs to be predicted. In order to solve the drawbacks of BP neural network, genetic algorithm and RBF neural network (GA-RBFNN) is presented to forecast electricity consumption in the study, and genetic algorithm is introduced and tried in optimizing the parameters of RBF neural network...
To resolve the training problem of high dimension BP neural network with limited small samples, this paper puts forward the concept of loosely and tightly grouping-cascaded BP network model, the definition of equivalence with BP neural network, and relative theorem. On the base of constructing the grouping-cascaded model which is proved equivalent to BP network, the required training sample numbers...
As the Government's macroeconomic regulation and control means, finance plays a significant role, but finance bears some certain risks. How to manage and prevent this risk is directly related to the survival and development of the whole country. This paper focuses on the combined model application research, fiscal risk early-warning based on principal component analysis and BP neural network. The...
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