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...
详细信息
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.
An improved bp algorithm for pattern recognition is proposed in this *** a function substitution for error measure,it resolves the inconsistency of bp algorithm for pattern recognition problems,*** quadratic error is ...
详细信息
An improved bp algorithm for pattern recognition is proposed in this *** a function substitution for error measure,it resolves the inconsistency of bp algorithm for pattern recognition problems,*** quadratic error is not sensitive to whether the training pattern is recognized correctly or *** by this new method,the computer simulation result shows that the convergence speed is increased to treble and performance of the network is better than conventional bp algorithm with momentum and adaptive step size.
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...
详细信息
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.
An improved bp algorithm for pattern recognition is proposed in this *** the difference between the real output and the desired output is transformed with a function,the training error in the improved algorithm is mor...
详细信息
An improved bp algorithm for pattern recognition is proposed in this *** the difference between the real output and the desired output is transformed with a function,the training error in the improved algorithm is more consistent with the misclassification *** results show that the new algorithm works well in pattern recognition field and it converges much faster than conventional bp algorithm.
This paper presents a half-autonomous navigation method of the Lunar Vehicle, then discuss the implementation of the case-based learning method by using Neural Network. At the end, we discuss the improvement of bp alg...
详细信息
ISBN:
(纸本)7312012035
This paper presents a half-autonomous navigation method of the Lunar Vehicle, then discuss the implementation of the case-based learning method by using Neural Network. At the end, we discuss the improvement of bp algorithm using Adaptive Learning method.
A new synthetic method of neural network and mechanistic model is presented in this paper. This method merges the mechanistic model knowledge with the neural network structure. It makes the number of weights of networ...
详细信息
ISBN:
(纸本)7312012035
A new synthetic method of neural network and mechanistic model is presented in this paper. This method merges the mechanistic model knowledge with the neural network structure. It makes the number of weights of network greatly reduce. The learning speed, nonlinear approximation precision and generalization performance of the network are improved. The effect of application in rolling force prediction is very excellent.
This paper proposes a short-term load forecasting method based on combination of ANN and fuzzy control. Improved bp algorithm is obtained by optimizing training samples, varying step and factor. In order to eliminate ...
详细信息
This paper proposes a short-term load forecasting method based on combination of ANN and fuzzy control. Improved bp algorithm is obtained by optimizing training samples, varying step and factor. In order to eliminate forecast error, on-line self-tuning fuzzy control is used. Simulation results demonstrate that the proposed method improves the forecast precision.
In this paper a new second order recursive learning algorithm to multilayer feedforward network is proposed. This algorithm makes not only each layer errors of network but also second order derivative information fact...
详细信息
ISBN:
(纸本)7312012035
In this paper a new second order recursive learning algorithm to multilayer feedforward network is proposed. This algorithm makes not only each layer errors of network but also second order derivative information factors backpropagate. And it is proved that it is equivalent to Newton iterative algorithm and has second order convergent speed. New algorithm achieves the recurrence calculation of Newton search directions and the inverse of Hessian matrices. Its calculation quantity is correspond to that of common recursive least squares algorithm. It is stated clearly that this new algorithm is superior to Karayiannis' second order algorithm according to analysis of their properties.
It is a common method to handle with nonlinear problems by linear models. While the parameter estimation achieved by this method is biased, the precision of its curve fitting is low. In this paper, we adopt the techni...
详细信息
It is a common method to handle with nonlinear problems by linear models. While the parameter estimation achieved by this method is biased, the precision of its curve fitting is low. In this paper, we adopt the technique of artificial neural network (ANN) to process nonlinear regression analysis and seek for the solution of analyzing dynamic state of groundwater level. The result is satisfactory.
A fast learning algorithm for training multilayer feedforward neural networks (FNN's) by using a fading memory extended Kalman filter (FMEKF) is presented first, along with a technique using a self-adjusting time-...
详细信息
A fast learning algorithm for training multilayer feedforward neural networks (FNN's) by using a fading memory extended Kalman filter (FMEKF) is presented first, along with a technique using a self-adjusting time-varying forgetting factor. Then a U-D factorization-based FMEKF is proposed to further improve the learning rate and accuracy of the FNN, In comparison with the backpropagation (bp) and existing EKF-based learning algorithms, the proposed U-D factorization-based FMEKF algorithm provides much more accurate learning results, : using fewer hidden nodes. It has improved convergence rate and numerical stability (robustness). In addition, it is less sensitive to start-up parameters (e.g., initial weights and covariance matrix) and the randomness in the observed data. It also has good generalization ability and needs less training time to achieve a specified learning accuracy. Simulation results in modeling and identification of nonlinear dynamic systems are given to show the effectiveness and efficiency of the proposed algorithm.
暂无评论