Because the driver's speed control system is a system with strong time-varying and large variation of parameters, in order to simulate the change rule of the driver's speed, this paper establishes the bp neura...
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ISBN:
(数字)9780784482933
ISBN:
(纸本)9780784482933
Because the driver's speed control system is a system with strong time-varying and large variation of parameters, in order to simulate the change rule of the driver's speed, this paper establishes the bp neural network PID model. In this paper, a three-layer bp neural network is designed and compared with the simulation curve of the speed variation of the classical PID control and the speed variation simulation curve of the PID control of the bp neural network in the PID control system. The simulation results of this paper show that the bp neural network PID controller designed in this paper shows good control effect in the performance dynamic following, real-time and robustness.
The Production of the quality steel is achieved by melting iron and steel scraps by utilizing electric power in an electric arc furnace, it acts as one of the major troublesome load in the electric power system due to...
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With the continuous in-depth study of metal cutting mechanism and the development of computer technology, people have established a computer-aided optimization program system for cutting data, which provides new metho...
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ISBN:
(纸本)9781728186160
With the continuous in-depth study of metal cutting mechanism and the development of computer technology, people have established a computer-aided optimization program system for cutting data, which provides new methods and means for selecting the optimal cutting parameters. As a new technology in the field of artificial intelligence research, neural networks have non-linear characteristics and information distribution. When dealing with multiple input and multiple output systems, it eliminates the complicated correlation analysis of various variables required by traditional modeling methods. The purpose of this article is to study the application and development of neural network technology for mechanical automation processing parameters. This article trains the sample set, learns the statistical law of the sample set, and saves the learned information in the weight. When the non-sample set mode is input, the bp network in the ideal neural network is highly nonlinear. The mapping ability is not limited by the number of inputs and outputs. In specific research applications, the original program can be freely modified as needed. This paper uses bp network as a research tool, trains bp network by using a large amount of experimental data, studies and analyzes several influencing factors of error remapping phenomenon, and uses bp network to solve the basic method of remapping problem, and initially established the feasibility of the method. Experimental research shows that this article is the ideal output data (actual data) for network testing and the network output data. From these data, it can be seen that the network training output and the ideal output error are controlled below 5%. It can be seen that the training result of the network is successful.
Sensors in the primary circuit of a pressurized water reactor (PWR) are normally designed with redundant structures to improve system safety and reliability. However, reliability of the actual system is often lower th...
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Sensors in the primary circuit of a pressurized water reactor (PWR) are normally designed with redundant structures to improve system safety and reliability. However, reliability of the actual system is often lower than that obtained by theoretical calculation due to the inevitable occurrence of common mode fault (CMF), which is a dependent failure event that can cause multiple failures in redundant channels. CMF may increase the reliability deviation of the system by orders of magnitude and, hence, seriously affects the reliability of the system. To mitigate the CMF of redundant sensors in nuclear power plants, an artificial neural network (ANN) can serve as a data-driven analytic model to monitor sensor parameters, to identify any possible abnormal status of the sensors, and provide an early warning. In this study, by using the high-fidelity dataset obtained in a full-scope PWR simulator as training, validation, and test data, a relevant parameter-based ANN black-box model (RPANN) was established by employing the back-propagation (bp) learning algorithm, which was then defined as an analytic redundancy. Time series-based ANN checking models (TSANNs) were also established for each of the input and output parameters of the RPANN in order to identify its abnormal state based on historical data in the past. When combined with the existing hardware redundancy, the ANN-based analytic redundancy can serve as an online monitoring tool of the hardware status and an online diagnosis strategy for sensor faults. Furthermore, ANN-based analytic redundancy can replace faulty hardware sensors to analytically reconstruct the reading of the monitored sensor parameter without having to reduce the reactor output power or even shut down the reactor for emergency maintenance so that the on-site calibration frequency of hardware sensors in redundant channels can be effectively reduced. This is not only of vital importance in reducing operation and maintenance costs of existing PWR powe
The Differential Evolution-Support Vector Regression (DE-SVR) algorithm is designed to model the small-signal intrinsic noise behavior of GaN HEMT. It not only overcomes the local minimization shortcoming of the Back ...
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The Differential Evolution-Support Vector Regression (DE-SVR) algorithm is designed to model the small-signal intrinsic noise behavior of GaN HEMT. It not only overcomes the local minimization shortcoming of the Back Propagation (bp) algorithm, but also uses the DE (Differential Evolution) algorithm to obtain the best parameter c (punishment factor) and parameter g (variance of kernel function) of the Support Vector Regression (SVR) algorithm. In order to validate the superiority of the DE-SVR algorithm, the experiment compares the modeling effects of bp algorithm, SVR algorithm, and DE-SVR algorithm in modeling the small-signal intrinsic noise model of GaN HEMT. The experimental results show that there are obvious advantages for the DE-SVR algorithm in modeling the small-signal intrinsic noise characteristics of GaN HEMT.
Traditional linear regression is the primary factor that affects measurement precision in measuring moisture content with microwave resonator. A regression is put forward based on an improved bp algorithm to modify th...
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ISBN:
(纸本)9781728181233
Traditional linear regression is the primary factor that affects measurement precision in measuring moisture content with microwave resonator. A regression is put forward based on an improved bp algorithm to modify the measurement result. First, the regression neural network is pre optimized by using the macro search ability, parallel operation and strong robustness of genetic algorithm. Then, integrating the gradient descent method of bp algorithm, the presented algorithm can effectively avoid the traditional bp algorithm of falling into local minimum, at the same time, high prediction accuracy and fast convergence speed are maintained. It has the characteristics of global superiority and accuracy for optimization, thus improving the measurement accuracy. The experimental results show that the mean square error between predicted moisture and actual moisture is 0.0109, the average absolute error is 0.0702, the average relative error is 0.1161, and the determination coefficient is 0.9989.
A deep learning method for improving the performance of polar belief propagation (bp) decoder equipped with a one-bit quantizer is proposed. The method generalizes the standard polar bp algorithm by assigning weights ...
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A deep learning method for improving the performance of polar belief propagation (bp) decoder equipped with a one-bit quantizer is proposed. The method generalizes the standard polar bp algorithm by assigning weights to the layers of the unfolded factor graph. These weights can be learned autonomously using deep learning techniques. We prove that the improved polar bp decoder has a symmetric structure, so that the weights can be trained by an all-zero codeword rather than an exponential number of codewords. In order to accelerate the training convergence, a layer-based weight assignment scheme is designed, which decreases the amount of trainable weights. Simulation results show that the improved polar bp decoder with a one-bit quantizer outperforms the standard polar bp decoder with a 2-bit quantizer and achieves faster convergence.
GB-MIMO (Ground-Based Multiple Input Multiple Output) radar plays an important role in landslide disaster prevention. Common MIMO radar imaging based on bp (Back Projection) algorithm is slow, and severely affects the...
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Intuitionistic fuzzy Petri net is an important class of Petri nets,which can be used to model the knowledge base system based on intuitionistic fuzzy production *** order to solve the problem of poor self-learning abi...
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Intuitionistic fuzzy Petri net is an important class of Petri nets,which can be used to model the knowledge base system based on intuitionistic fuzzy production *** order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems,a new Petri net modeling method is proposed by introducing bp(Error Back Propagation)algorithm in neural *** judging whether the transition is ignited by continuous function,the intuitionistic fuzziness of classical bp algorithm is extended to the parameter learning and training,which makes Petri network have stronger generalization ability and adaptive function,and the reasoning result is more accurate and credible,which is useful for information ***,a typical example is given to verify the effectiveness and superiority of the parameter optimization method.
We present a quantum bp neural network with the universality of single-qubit rotation gate and two-qubit Controlled-NOT gate. Also, we show the process of the bp learning algorithm for the quantum model, and propose a...
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We present a quantum bp neural network with the universality of single-qubit rotation gate and two-qubit Controlled-NOT gate. Also, we show the process of the bp learning algorithm for the quantum model, and propose an improved bp learning algorithm based on quantum genetic algorithm. The type recognition simulation of the Matlab program shows the efficiencies of the quantum neural network and the improved learning algorithm.
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