This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorith...This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.展开更多
A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the rel...A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information.Then the sub-pixel scaled target could be predicted by the trained model.In order to improve the performance of BP network,BP learning algorithm with momentum was employed.The experiments were conducted both on synthetic images and on hyperspectral imagery(HSI).The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.展开更多
Convolutional Neural Networks(CNNs)models succeed in vast domains.CNNs are available in a variety of topologies and sizes.The challenge in this area is to develop the optimal CNN architecture for a particular issue in...Convolutional Neural Networks(CNNs)models succeed in vast domains.CNNs are available in a variety of topologies and sizes.The challenge in this area is to develop the optimal CNN architecture for a particular issue in order to achieve high results by using minimal computational resources to train the architecture.Our proposed framework to automated design is aimed at resolving this problem.The proposed framework is focused on a genetic algorithm that develops a population of CNN models in order to find the architecture that is the best fit.In comparison to the co-authored work,our proposed framework is concerned with creating lightweight architectures with a limited number of parameters while retaining a high degree of validity accuracy utilizing an ensemble learning technique.This architecture is intended to operate on low-resource machines,rendering it ideal for implementation in a number of environments.Four common benchmark image datasets are used to test the proposed framework,and it is compared to peer competitors’work utilizing a range of parameters,including accuracy,the number of model parameters used,the number of GPUs used,and the number of GPU days needed to complete the method.Our experimental findings demonstrated a significant advantage in terms of GPU days,accuracy,and the number of parameters in the discovered model.展开更多
As a most popular learning algorithm for the feedforward neural networks, the classic BP algorithm has its many shortages. To overcome some of the shortages, a modified learning algorithm is proposed in the article. A...As a most popular learning algorithm for the feedforward neural networks, the classic BP algorithm has its many shortages. To overcome some of the shortages, a modified learning algorithm is proposed in the article. And the simulation result illustrate the modified algorithm is more effective and practicable.展开更多
Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensationbased recurrent fuzzy neural network (CRFNN) by adding recurrent element and compensatory element to the conventional...Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensationbased recurrent fuzzy neural network (CRFNN) by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure identification of the CRFNN in order to confirm the fuzzy rules and their correlative parameters effectively. Furthermore, we improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.展开更多
Based on the critical position of the endpoint quality prediction for basic oxygen furnaces (BOFs) in steelmaking, and the latest results in computational intelligence (C1), this paper deals with the development ...Based on the critical position of the endpoint quality prediction for basic oxygen furnaces (BOFs) in steelmaking, and the latest results in computational intelligence (C1), this paper deals with the development of a novel memetic algorithm (MA) for neural network (NN) lcarnmg. Included in this is the integration of extremal optimization (EO) and Levenberg-Marquardt (LM) pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.展开更多
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...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.展开更多
The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network st...The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm展开更多
Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be...Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.展开更多
机械钻速(rate of penetration,ROP)是钻井作业优化和减少成本的关键因素,钻井时有效地预测ROP是提升钻进效率的关键。由于井下钻进时复杂多变的情况和地层的非均质性,通过传统的ROP方程和回归分析方法来预测钻速受到了一定的限制。为...机械钻速(rate of penetration,ROP)是钻井作业优化和减少成本的关键因素,钻井时有效地预测ROP是提升钻进效率的关键。由于井下钻进时复杂多变的情况和地层的非均质性,通过传统的ROP方程和回归分析方法来预测钻速受到了一定的限制。为了实现对钻速的高精度预测,对现有BP (back propagation)神经网络进行优化,提出了一种新的神经网络模型,即动态自适应学习率的粒子群优化BP神经网络,利用录井数据建立目标井预测模型来对钻速进行预测。在训练过程中对BP神经网络进行优化,利用启发式算法,即附加动量法和自适应学习率,将两种方法结合起来形成动态自适应学习率的BP改进算法,提高了BP神经网络的训练速度和拟合精度,获得了更好的泛化性能。将BP神经网络与遗传优化算法(genetic algorithm,GA)和粒子群优化算法(particle swarm optimization,PSO)结合,得到优化后的动态自适应学习率BP神经网络。研究利用XX8-1-2井的录井数据进行实验,对比BP神经网络、PSO-BP神经网络、GA-BP神经网络3种不同的改进后神经网络的预测结果。实验结果表明:优化后的PSO-BP神经网络的预测性能最好,具有更高的效率和可靠性,能够有效的利用工程数据,在有一定数据采集量的区域提供较为准确的ROP预测。展开更多
This paper proposes a novel recursive partitioning method based on constrained learning neural networks to find an arbitrary number (less than the order of the polynomial) of (real or complex) roots of arbitrary polyn...This paper proposes a novel recursive partitioning method based on constrained learning neural networks to find an arbitrary number (less than the order of the polynomial) of (real or complex) roots of arbitrary polynomials. Moreover, this paper also gives a BP network constrained learning algorithm (CLA) used in root-finders based on the constrained relations between the roots and the coefficients of polynomials. At the same time, an adaptive selection method for the parameter d P with the CLA is also given. The experimental results demonstrate that this method can more rapidly and effectively obtain the roots of arbitrary high order polynomials with higher precision than traditional root-finding approaches.展开更多
基金Supported by the National 863 CIMS Project Foundation(863-511-010)Tianjin Natural Science Foundation(983602011)Backbone Young Teacher Project Foundation of Ministry of Education
文摘This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.
基金Sponsored by the National Natural Science Foundation of China(Grant No. 60272073, 60402025 and 60802059)by Foundation for the Doctoral Program of Higher Education of China (Grant No. 200802171003)
文摘A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information.Then the sub-pixel scaled target could be predicted by the trained model.In order to improve the performance of BP network,BP learning algorithm with momentum was employed.The experiments were conducted both on synthetic images and on hyperspectral imagery(HSI).The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.
文摘Convolutional Neural Networks(CNNs)models succeed in vast domains.CNNs are available in a variety of topologies and sizes.The challenge in this area is to develop the optimal CNN architecture for a particular issue in order to achieve high results by using minimal computational resources to train the architecture.Our proposed framework to automated design is aimed at resolving this problem.The proposed framework is focused on a genetic algorithm that develops a population of CNN models in order to find the architecture that is the best fit.In comparison to the co-authored work,our proposed framework is concerned with creating lightweight architectures with a limited number of parameters while retaining a high degree of validity accuracy utilizing an ensemble learning technique.This architecture is intended to operate on low-resource machines,rendering it ideal for implementation in a number of environments.Four common benchmark image datasets are used to test the proposed framework,and it is compared to peer competitors’work utilizing a range of parameters,including accuracy,the number of model parameters used,the number of GPUs used,and the number of GPU days needed to complete the method.Our experimental findings demonstrated a significant advantage in terms of GPU days,accuracy,and the number of parameters in the discovered model.
文摘As a most popular learning algorithm for the feedforward neural networks, the classic BP algorithm has its many shortages. To overcome some of the shortages, a modified learning algorithm is proposed in the article. And the simulation result illustrate the modified algorithm is more effective and practicable.
基金Supported by the National High-Tech Research and Development Program of China (Grant No. 2006AA05A107)Special Fund of JiangsuProvince for Technology Transfer (Grant No. BA2007008)
文摘Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensationbased recurrent fuzzy neural network (CRFNN) by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure identification of the CRFNN in order to confirm the fuzzy rules and their correlative parameters effectively. Furthermore, we improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.
基金Project (No. 60721062) supported by the National Creative Research Groups Science Foundation of China
文摘Based on the critical position of the endpoint quality prediction for basic oxygen furnaces (BOFs) in steelmaking, and the latest results in computational intelligence (C1), this paper deals with the development of a novel memetic algorithm (MA) for neural network (NN) lcarnmg. Included in this is the integration of extremal optimization (EO) and Levenberg-Marquardt (LM) pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.
文摘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.
文摘The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm
基金supported by the National Natural Science Foundation of China(Grant No.51809279)the Major National Science and Technology Program(Grant No.2016ZX05028-001-05)+1 种基金Program for Changjiang Scholars and Innovative Research Team in University(Grant No.IRT14R58)the Fundamental Research Funds for the Central Universities,that is,the Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment(Grant No.20CX02302A).
文摘Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.
文摘This paper proposes a novel recursive partitioning method based on constrained learning neural networks to find an arbitrary number (less than the order of the polynomial) of (real or complex) roots of arbitrary polynomials. Moreover, this paper also gives a BP network constrained learning algorithm (CLA) used in root-finders based on the constrained relations between the roots and the coefficients of polynomials. At the same time, an adaptive selection method for the parameter d P with the CLA is also given. The experimental results demonstrate that this method can more rapidly and effectively obtain the roots of arbitrary high order polynomials with higher precision than traditional root-finding approaches.