进化神经网络PID控制器的研究与应用

提出一种基于进化神经网络的PID控制器设计方法.该控制器主要由3部分组成,第1部分应用神经网络根据控制对象的输入、输出在线调整PID控制器参数.第2部分利用进化算法根据性能指标对神经网络控制器参数进行优化,找出最优的神经网络初始权系数和比例系数.第3部分是传统PID控制器.把该控制器温度控制的仿真对照结果表明,这种控制算法具有结构简单、鲁棒适应性强、进化性能良好的特点.同时还提出一种以快速响应为目标的改进方案.     

A novel fused neural network controller for lateral control of autonomous vehicles

We present a novel fused feed-forward neural network controller inspired by the notion of task decomposition principle. The controller is structurally simple and can be applied to a class of control systems that their control requires manipulation of two input variables. The benchmark problem of inverted pendulum is such example that its control requires availability of the angle as well as the displacement. We demonstrate that the lateral control of autonomous vehicles belongs to this class of systems and successfully apply the proposed controller to this problem. The parameters of the controller are encoded into real value chromosomes for genetic algorithm (GA) optimization. The neural network controller contains three neurons and six connection weights implying a small search space implying faster optimization time due to few controller parameters. The controller is also tested on two benchmark control problems of inverted pendulum and the ball-and-beam system. In particular, we apply the controller to lateral control of a prototype semi-autonomous vehicle. Simulation results suggest a good performance for all the tested systems. To demonstrate the robustness of the controller, we conduct Monte-Carlo evaluations when the system is subjected to random parameter uncertainty. Finally experimental studies on the lateral control of a prototype autonomous vehicle with different speed of operation are included. The simulation and experimental studies suggest the feasibility of this controller for numerous applications.     

基于单神经元PID的航空发动机解耦控制

将神经网络应用到PID控制器的参数整定过程中,提出了一种基于改进单神经元PID的航空发动机解耦控制方法,通过在航空涡扇发动机多变量控制系统中的应用,得出了实际的仿真结果及结论。仿真结果表明,该改进单神经元PID解耦控制方法与传统的PID多变量解耦相比,具有响应速度快,自适应能力强,抗干扰能力强,实现简单的优点,因而可以广泛的应用于非线性系统的解耦控制中。     

A neural network based adaptive robot controller

Neural network based adaptive controllers have been shown to achieve much improved accuracy compared with traditional adaptive controllers when applied to trajectory tracking in robot manipulators. This paper describes a new Recursive Prediction Error technique for estimating network parameters which is more computationally efficient. Results show that this neural controller suppresses disturbances accurately and achieves very small errors between commanded and actual trajectories.     

Reliability estimation for neural network based autonomous driving

This paper describes a technique called Input Reconstruction Reliability Estimation (IRRE) for determining the response reliability of a restricted class of multi-layer perceptrons (MLPs). The technique uses a network's ability to accurately encode the input pattern in its internal representation as a measure of its reliability. The more accurately a network is able to reconstruct the input pattern from its internal representation, the more reliable the network is considered to be. IRRE provides a good estimate of the reliability of MLPs trained for autonomous driving. Results are presented in which the reliability estimates provided by IRRE are used to select between networks trained for different driving situations.     

用人工神经网络实现秘密共享

以人工神经网络为基础实现了一种秘密共享方案.该方案不同于已有的一些秘密共享方案,它利用人工神经网络分类、识别的性质,将参与恢复秘密的用户组合类比为人工神经网络的输入序列,通过训练,人工神经网络可以识别正确的用户组合并得到原始秘密.该秘密共享方案可以实现不同权限的用户所参与的秘密共享方案,但是不会增加存储或计算上的开销.     

基于SoPC的神经网络速度控制器的实现

一种基于 SoPC 的神经网络速度控制器的设计方案。速度控制器采用神经网络参数辨识自适应控制,以现场可编程门阵列(FPGA)为硬件平台,用 Nios Ⅱ软核处理器作为上位机,实现一个完整的速度控制器的片上可编程系统(SoPC)。实验结果表明,该控制系统能够满足现代速度控制系统高速度、高精度的要求。     

Estimation of all-terminal network reliability using an artificial neural network

The exact calculation of all-terminal network reliability is an NP-hard problem, with computational effort growing exponentially with the number of nodes and links in the network. During optimal network design, a huge number of candidate topologies are typically examined with each requiring a network reliability calculation. Because of the impracticality of calculating all-terminal network reliability for networks of moderate to large size, Monte Carlo simulation methods to estimate network reliability and upper and lower bounds to bound reliability have been used as alternatives. This paper puts forth another alternative to the estimation of all-terminal network reliability — that of artificial neural network (ANN) predictive models. Neural networks are constructed, trained and validated using the network topologies, the link reliabilities, and a network reliability upperbound as inputs and the exact network reliability as the target. A hierarchical approach is used: a general neural network screens all network topologies for reliability followed by a specialized neural network for highly reliable network designs. Both networks with identical link reliability and networks with varying link reliability are studied. Results, using a grouped cross-validation approach, show that the ANN approach yields more precise estimates than the upperbound, especially in the worst cases. Using the reliability estimation methods of the ANN, the upperbound and backtracking, optimal network design by simulated annealing is considered. Results show that the ANN regularly produces superior network designs at a reasonable computational cost.Scope and purposeAn important application area of operations research is the design of structures, products or systems where both technical and business aspects must be considered. One expanding design domain is the design of computer or communications networks. While cost is a prime consideration, reliability is equally important. A common reliability measure is all-terminal reliability, the probability that all nodes (computers or terminals) on the network can communicate with all others. Exact calculation of all-terminal reliability is an NP-hard problem, precluding its use during optimal network topology design, where this calculation must be made thousands or millions of times. This paper presents a novel computationally practical method for estimating all-terminal network reliability. Is shown how a neural network can be used to estimate all-terminal network reliability by using the network topology, the link reliabilities and an upperbound on all-terminal network reliability as inputs. The neural network is trained and validated on a very minute fraction of possible network topologies, and once trained, it can be used without restriction during network design for a topology of a fixed number of nodes. The trained neural network is extremely fast computationally and can accommodate a variety of network design problems. The neural network approach, an upper bound approach and an exact backtracking calculation are compared for network design using simulated annealing for optimization and show that the neural network approach yields superior designs at manageable computational cost.     

一种神经网络分数阶PID控制器的实现

为解决分数阶PID控制器参数难于整定的问题,设计了一种基于神经网络的分数阶PID控制器。通过采用反向传播（ BP）神经网络的参数调节策略,可以实现一种五维参数自学习的PID控制器。将分数阶PID控制器数字化,通过BP算法调节神经网络突触权值,经过调整的神经网络输出作为分数阶PID控制器的参数。经过仿真验证,神经网络分数阶PID控制器比传统PID控制器精度提高6倍且控制更加稳定。     

基于模糊神经网络的统一潮流控制器

为了减少先验知识对统一潮流控制器中模糊规则的设计和电力系统参数的变化对统一潮流控制器性能的影响,文中采用模糊神经网络来设计统一潮流控制器.为此首先简单介绍了统一潮流控制器的控制策略,然后阐述了自组织模糊神经网络和基于遗传算法的模糊神经网络的构造方法,接着将自组织模糊神经网络、基于遗传算法的模糊神经网络结合统一潮流控制器的控制策略应用于两种统一潮流控制器.最后通过MATLAB仿真例子来验证:这两种统一潮流控制器的设计方法的有效性.     

Stable nonlinear adaptive controller for an autonomous underwater vehicle using neural networks

In general, the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions. For this reason, high performance control system for an AUV usually should have the capacities of learning and adaptation to the time-varying dynamics of the vehicle. In this article, we present a robust adaptive nonlinear control scheme for an AUV, where a linearly parameterized neural network (LPNN) is introduced to approximate the uncertainties of the vehicle's dynamics, and the basis function vector of the network is constructed according to the vehicle's physical properties. The proposed control scheme can guarantee that all of the signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.     

基于PID神经网络控制微信号发生装置

高压电网中电子式互感器输出信号一般为毫伏级模拟信号,在高压保护、测量装置研发试验中需要能够提供高精度微小信号的继电保护测试仪.文中介绍了一种基于PID神经元网络负反馈控制技术和FPGA控制高精度串行DA转换输出精准微小信号的系统设计方案,分析了装置为提供高稳定度小信号所采用的新方法,设计了基于现场可编程门阵列(FPGA)技术的16位并行数字信号串行输出控制模块.系统通过嵌入微控制器的正弦波形产生算法和自适应PID控制算法输出高拟合离散正弦波数字信号.由FPGA控制输出的各路离散信号经串口DA转换、滤波后输出的模拟电压信号稳定可靠、频带宽、动态特性良好.该测试仪输出的小信号电压变化比差测量值满足0.2级电子式互感器准确度的要求.     

一种基于蚁群算法的粗神经网络PID控制器

针对传统的线性PID控制器参数难以实现在线调整缺陷,设计了一种新的基于粗神经网络的PID控制器,利用粗神经元代替传统神经元,扩展了网络的应用范围,并采用蚁群算法优化神经网络连接权初值,使神经网络连接权初值不再靠经验随机选取,从而得到最优参数的控制器。仿真结果表明,该控制器具有良好的控制性能。     

基于人工神经网络的参数灵敏度分析模型*

通过人工神经网络算法与参数灵敏度分析的结合,找到了一种新的工程系统功能模拟和变化分析方法。神经网络可以有效地解决复杂、非线性系统的功能模拟问题,其传递函数的可微性为参数灵敏度矩阵的求解提供了保证,从而方便寻找系统输入属性与输出属性之间的影响因子。同时,该模型具有良好的扩展性,可以更加全面地考虑系统影响因素。经实例仿真分析表明：该方法在工程分析方面,能够快速找到属性之间的关联程度,得到准确、稳定的分析结果,满足工程分析需求。     

Realization of emergent behavior in collective autonomous mobile agents using an artificial neural network and a genetic algorithm

This paper proposes a pursuit system that utilizes the artificial life concept where autonomous mobile agents emulate the social behavior of animals and insects and realize their group behavior. Each agent contains sensors to perceive other agents in several directions, and decides its behavior based on the information obtained by these sensors. In this paper, a neural network is used for behavior decision controlling. The input of the neural network is decided by the existence of other agents, and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behavior adequately fit the goal and can express group behavior. The validity of the system is verified through simulation. Also in this paper, we have observed the agents emergent behavior during simulation.This paper was supported by WonKwang University in 2004.     

高速公路匝道回归神经网络控制器的设计

提出用回归神经网络进行入口匝道控制的思路。阐述了Elman回归神经网络原理与入口匝道控制原理,选取上、下游时间占有率和车速作为匝道控制器的输入量,并设计了Elman回归神经网络入口匝道控制器,采用一种改进的算法对回归神经网络进行训练。仿真实验表明,该控制器学习误差小,泛化能力好,具有良好的应用前景。     

Classifying inventory using an artificial neural network approach

This paper presents artificial neural networks (ANNs) for ABC classification of stock keeping units (SKUs) in a pharmaceutical company. Two learning methods were utilized in the ANNs, namely back propagation (BP) and genetic algorithms (GA). The reliability of the models was tested by comparing their classification ability with two data sets (a hold-out sample and an external data set). Furthermore, the ANN models were compared with the multiple discriminate analysis (MDA) technique. The results showed that both ANN models had higher predictive accuracy than MDA. The results also indicate that there was no significant difference between the two learning methods used to develop the ANN.     

Dynamic weight estimation using an artificial neural network

A Multi-Layer Perceptron Artificial Neural Network is employed to enable the mass that is applied to a weighing platform to be rapidly and accurately estimated before the platform has settled to the steady state. This is achieved through training the network on a set of waveforms resulting from applied masses over the operating range of the weighing platform. Results are given for both simulated and experimental data that confirm the success of the method.