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关于人工神经网络学科的思考 被引量:2
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作者 徐长安 《中国科教创新导刊》 2009年第1期58-58,共1页
20世纪是科学技术大发展的世纪,是人类文明的新篇章.经过近半个世纪的努力探索,一门崭新的向人类自身大脑学习的新学科——人工神经网络(ArtificialNeuralNetwork,ANN),已经诞生并正在茁壮成长.通过本文介绍使大家对人工神经网络的概... 20世纪是科学技术大发展的世纪,是人类文明的新篇章.经过近半个世纪的努力探索,一门崭新的向人类自身大脑学习的新学科——人工神经网络(ArtificialNeuralNetwork,ANN),已经诞生并正在茁壮成长.通过本文介绍使大家对人工神经网络的概念、特点、主要方向和多学科性有较深入的理解和掌握. 展开更多
关键词 人工神经网络 自适应性神经网络
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利用地震属性、多元统计分析理论和ANFIS预测碳酸盐岩储层裂缝孔隙度 被引量:7
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作者 刘晓梅 孙勤华 +1 位作者 刘建新 刘伟方 《测井技术》 CAS CSCD 北大核心 2009年第3期257-260,共4页
碳酸盐岩储层非均质性和各向异性强,利用某种单一技术难以准确定量地刻画出裂缝孔隙度空间分布情况。利用地震属性、多元统计分析理论和ANFIS(自适应性模糊神经网络)综合预测碳酸盐岩储层裂缝孔隙度。用成像测井资料准确识别裂缝孔隙发... 碳酸盐岩储层非均质性和各向异性强,利用某种单一技术难以准确定量地刻画出裂缝孔隙度空间分布情况。利用地震属性、多元统计分析理论和ANFIS(自适应性模糊神经网络)综合预测碳酸盐岩储层裂缝孔隙度。用成像测井资料准确识别裂缝孔隙发育位置,并计算出裂缝孔隙度的大小,用自适应性模糊神经网络建立井孔裂缝孔隙度和井旁地震属性数据体之间的函数关系模型,结合全区地震属性数据体对碳酸盐岩储层进行定量评价。利用该项技术预测塔中某工区碳酸盐岩储层裂缝孔隙度分布,预测结果与钻遇优质储层的井点吻合,说明该方法在该区有一定的实用性。 展开更多
关键词 测井解释 裂缝孔隙度 成像测井 地震属性 多元统计分析 自适应性模糊神经网络
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沈阳市商场冬季室内热舒适的模糊分析 被引量:4
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作者 牛润萍 张培红 陈其针 《暖通空调》 北大核心 2006年第5期116-118,共3页
对沈阳市冬季商场室内热环境状况进行了实地调查,建立了基于自适应性模糊神经网络的人体热舒适模糊评判模型,对采集到的数据进行了模糊推理和网络训练,总结出了关于人体热舒适的模糊推理规则。由仿真结果可知,模糊评判模型输出值与热感... 对沈阳市冬季商场室内热环境状况进行了实地调查,建立了基于自适应性模糊神经网络的人体热舒适模糊评判模型,对采集到的数据进行了模糊推理和网络训练,总结出了关于人体热舒适的模糊推理规则。由仿真结果可知,模糊评判模型输出值与热感觉投票值吻合较好,为客观评价和预测人体热舒适提供了一种方法和思路。 展开更多
关键词 热舒适 隶属函数 自适应性模糊神经网络
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Identification and Control of Dynamical Systems Using Modified Neural Networks
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作者 任雪梅 陈杰 《Journal of Beijing Institute of Technology》 EI CAS 1999年第3期238-244,共7页
Aim To study the identification and control of nonlinear systems using neural networks. Methods A new type of neural network in which the dynamical error feedback is used to modify the inputs of the network was empl... Aim To study the identification and control of nonlinear systems using neural networks. Methods A new type of neural network in which the dynamical error feedback is used to modify the inputs of the network was employed to reduce the inherent network approximation error. Results A new identification model constructed by the proposed network and stable filters was derived for continuous time nonlinear systems, and a stable adaptive control scheme based on the proposed networks was developed. Conclusion Theory and simulation results show that the modified neural network is feasible to control a class of nonlinear systems. 展开更多
关键词 nonlinear systems neural networks adaptive control system identification
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Robust adaptive control for a class of uncertain non-affine nonlinear systems using neural state feedback compensation 被引量:1
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作者 赵石铁 高宪文 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第3期636-643,共8页
A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback c... A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach. 展开更多
关键词 adaptive control neural networks uncertain non-affine systems state feedback Lyapunov stability
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A novel robust adaptive controller for EAF electrode regulator system based on approximate model method
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作者 李磊 毛志忠 《Journal of Central South University》 SCIE EI CAS 2012年第8期2158-2166,共9页
The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative (PID) can not meet the req... The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative (PID) can not meet the requirements. A robust adaptive neural network controller (RANNC) for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 ℃/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller (INNC) and PID controller (PIDC). 展开更多
关键词 approximate model electric arc furnaces nonlinear control normalized radial basis function neural network (NRBFNN)
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