Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A 1-D distributed parameter model of a fin is developed from basic thermal physics principles. ldquoSnapshotrdquo solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the ldquoproper orthogonal decompositionrdquo (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.     

Training recurrent neurocontrollers for real-time applications.

In this paper, we introduce a new approach to train recurrent neurocontrollers for real-time applications. We begin with training a recurrent neurocontroller for robustness on high-fidelity models of physical systems. For training, we use a recently developed derivative-free Kalman filter method which we enhance for controller training. After training, we fix weights of our recurrent neurocontroller and deploy it in an embedded environment. Then, we carry out additional training of the neurocontroller by adapting in real time its internal state (short-term memory), rather than its weights (long-term memory). Such real-time training is done with a new combination of simultaneous perturbation stochastic approximation (SPSA) and adaptive critic. Our critic is also a recurrent neural network (RNN), and it is trained by stochastic meta-descent (SMD) for increased efficiency. Our approach is applied to two important practical problems, electronic throttle control and hybrid electric vehicle control, with apparent performance improvement.     

A multi-agent approach for dynamic channel allocation

We propose a multi‐agent approach for dynamic channel allocation (MA‐DCA) in mobile cellular networks. Our approach assumes that each cell in a cellular network works as an agent that negotiates its bandwidth (channel) requirements with its neighbors to minimize its call drop probability. Using stochastic simulation, we compare our MA‐DCA approach with simple fixed channel allocation (FCA) and dynamic channel borrowing approaches for different call arrival rates, cellular network sizes and number of available channels. The results of our experiments show that the proposed MA‐DCA approach performs better than dynamic channel borrowing and FCA approaches.     

Using a New Model of Recurrent Neural Network for Control

This paper shows the results obtained in controlling a mobile robot by means of local recurrent neural networks based on a radial basis function (RBF) type architecture. The model used has a Finite Impulse Response (FIR) filter feeding back each neuron's output to its own input, while using another FIR filter as a synaptic connection. The network parameters (coefficients of both filters) are adjusted by means of the gradient descent technique, thus obtaining the stability conditions of the process. As a practical application the system has been successfully used for controlling a wheelchair, using an architecture made up by a neurocontroller and a neuroidentifier. The role of the latter, connected up in parallel with the wheelchair, is to propagate the control error to the neurocontroller, thus cutting down the control error in each working cycle.     

Approximation of Gaussian basis functions in the problem of adaptive control of nonlinear objects

An approach to the development of a neurocontroller for controlling nonlinear dynamical objects on the basis of radial-basis function neural networks is considered. Piecewise-linear approximation of Gaussian basis functions is proposed to simplify the solution of the problem being considered. Simulation results show that the method allows one to reduce the time of construction of an object model and calculation of its control signal.     

Neural networks for advanced control of robot manipulators

Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.     

基于关键功能模块挖掘的蛋白质功能预测

精确注释蛋白质功能是从分子水平理解生物体的关键.由于内在的困难和昂贵的开销,实验方法注释蛋白质功能已经很难满足日益增长的序列数据.为此,提出了许多基于蛋白质相互作用（Protein-protein interaction,PPI）网络的计算方法预测蛋白质功能.当今蛋白质功能预测的趋势是融合蛋白质相互作用网络和异构生物数据.本文提出一种基于多关系网络中关键功能模块挖掘的蛋白质功能预测算法.关键功能模块由一组紧密联系且共享生物功能的蛋白质组成,它们能与网络中的剩余部分较好地区分开来.算法通过从多关系网络的每一个简单网络中挖掘高内聚、低耦合的子图形成关键功能模块.关键功能模块中邻居蛋白质的功能用于注释待预测功能的蛋白质.每一个简单网络在蛋白质功能预测中的重要性各不相同.实验结果表明,提出的方法性能优于现有的蛋白质功能预测方法.     

基于模糊AHP的CDMA网络资源利用率的评价研究

为了确保CDMA网络资源的合理利用,提出一种基于模糊AHP的CDMA无线网络资源利用效率的评价方法.采用yaahp层次分析软件通过对CDMA无线网络资源指标选择建立层次分析模型,确定了各指标的组合权重;利用模糊综合评判（FCA）方法建立模糊评价模型,计算CDMA网络资源利用的综合评价并做出决策.实际案例表明,该方法通过将层次分析法和模糊综合评价方法相结合,有效的对定性指标和定量指标进行综合权衡分析,评价模型科学、合理地反映出实际评价结果,为CDMA无线网络资源及时调配提供合理依据.     

A Neuro-Dynamic Programming-Based Optimal Controller for Tomato Seedling Growth in Greenhouse Systems

This work proposes a neuro-dynamic programming-based optimal controller to guide the growth of tomato seedling crops by manipulating its environmental conditions in a greenhouse. The neurocontroller manages the growth development of the crop, while minimizing a predefined cost function that considers the operative costs and the final state errors under physical constraints on process variables and actuator signals. The aim is to guide the growth of tomato seedlings by controlling the microclimate of the greenhouse. The design process of the neurocontroller considers the nonlinear dynamic behavior of the crop-greenhouse system model and the real climate data. Simulations of the proposed approach allow for contrasting its performance against those of other strategies for tomato seedling crop development subject to various climatic conditions.     

具有特征排序功能的鲁棒性模糊聚类方法

提出了一种加权模糊聚类算法, 其优势在于能在实现有效聚类的同时, 对样本噪音进行识别和按样本特征对聚类的贡献程度进行排序. 因此, 本文所提出的方法具有鲁棒性, 并可对所得的特征排序进行特征选择, 实验结果表明了该方法具有上述优势.     

Assessing semantic annotation activities with formal concept analysis

This paper describes an approach to assessing semantic annotation activities based on formal concept analysis (FCA). In this approach, annotators use taxonomical ontologies created by domain experts to annotate digital resources. Then, using FCA, domain experts are provided with concept lattices that graphically display how their ontologies were used during the semantic annotation process. In consequence, they can advise annotators on how to better use the ontologies, as well as how to refine these ontologies to better suit the needs of the semantic annotators. To illustrate the approach, we describe its implementation in @note, a Rich Internet Application (RIA) for the collaborative annotation of digitized literary texts, we exemplify its use with a case study, and we provide some evaluation results using the method.     

网络安全评估研究

文章提出了一种网络安全评估模型。该网络评估模型的实现过程是首先分析网络的重要资产,结合CC建立网络重要资产的安全功能组件,对安全功能组件评估,得到资产的功能组件的满足度值,最后采用多层线性加权法进行定量计算,由所有网络元素的安全指标综合确定网络安全的等级。     

基于自适应评价UPFC神经网络控制器设计

对含UPFC(统一潮流控制器)的电力系统提出一种新型的非线性最优神经网络控制器。启发式动态规划(HDP)是自适应评价设计(ACDs)体系中的一员,采用HDP来设计UPFC神经网络控制器。和传统的PI控制器相比,这种神经网络控制器能够提供非线性最优控制。仿真结果表明,此种控制器具有很好的控制效果。     

Neuromorphic learning of continuous-valued mappings fromnoise-corrupted data

The effect of noise on the learning performance of the backpropagation algorithm is analyzed. A selective sampling of the training set is proposed to maximize the learning of control laws by backpropagation, when the data have been corrupted by noise. The training scheme is applied to the nonlinear control of a cart-pole system in the presence of noise. The neural computation provides the neurocontroller with good noise-filtering properties. In the presence of plant noise, the neurocontroller is found to be more stable than the teacher. A novel perspective on the application of neural network technology to control engineering is presented.     

Fault-Tolerant Indirect Adaptive Neurocontrol for a Static Synchronous Series Compensator in a Power Network With Missing Sensor Measurements

Identification and control of nonlinear systems depend on the availability and quality of sensor measurements. Measurements can be corrupted or interrupted due to sensor failure, broken or bad connections, bad communication, or malfunction of some hardware or software (referred to as missing sensor measurements in this paper). This paper proposes a novel fault-tolerant indirect adaptive neurocontroller (FTIANC) for controlling a static synchronous series compensator (SSSC), which is connected to a power network. The FTIANC consists of a sensor evaluation and (missing sensor) restoration scheme (SERS), a radial basis function neuroidentifier (RBFNI), and a radial basis function neurocontroller (RBFNC). The SERS provides a set of fault-tolerant measurements to the RBFNI and RBFNC. The resulting FTIANC is able to provide fault-tolerant effective control to the SSSC when some crucial time-varying sensor measurements are not available. Simulation studies are carried out on a single machine infinite bus (SMIB) as well as on the IEEE 10-machine 39-bus power system, for the SSSC equipped with conventional PI controllers (CONVC) and the FTIANC without any missing sensors, as well as for the FTIANC with multiple missing sensors. Results show that the transient performances of the proposed FTIANC with and without missing sensors are both superior to the CONVC used by the SSSC (without any missing sensors) over a wide range of system operating conditions. The proposed fault-tolerant control is readily applicable to other plant models in power systems.      

A holonic approach based integration methodology for transportation and warehousing functions of the supply network

The supply chain (SC) is often defined as a network that is composed of different functions, including suppliers, manufacturing plants, warehouses/distribution centers, retailers and customers. A supply network (SN) is a sequence of different and multiple numbers of functions and individual functional units that must satisfy all capacities and demand requirements imposed by customers with minimum cost to the network. The most important functions of a SN are warehousing and transportation functions. This paper addresses the warehousing and transportation network design problem that involves determining the best strategy for distributing the sub-products from the suppliers to the warehouse and from the warehouse to the manufacturers. Considering some similarities between holonic systems and SN systems, a holonic approach based modeling methodology is proposed in this study. A multiple supplier, single warehouse and multiple manufacturer system are considered to be an integrated warehousing and transportation network. Consequently, a linear programming model is presented to maximize the profit of both of the overall SN and the individual functional units of the SN functions.     

Training Recurrent Neurocontrollers for Robustness With Derivative-Free Kalman Filter

We are interested in training neurocontrollers for robustness on discrete-time models of physical systems. Our neurocontrollers are implemented as recurrent neural networks (RNNs). A model of the system to be controlled is known to the extent of parameters and/or signal uncertainties. Parameter values are drawn from a known distribution. For each instance of the model with specified parameters, a recurrent neurocontroller is trained by evaluating sensitivities of the model outputs to perturbations of the neurocontroller weights and incrementally updating the weights. Our training process strives to minimize a quadratic cost function averaged over many different models. In the end, the process yields a robust recurrent neurocontroller, which is ready for deployment with fixed weights. We employ a derivative-free Kalman filter algorithm proposed by Norgaard and extended by Feldkamp (2001) and Feldkamp (2002) to neural network training. Our training algorithm combines effectiveness of a second-order training method with universal applicability to both differentiable and nondifferentiable systems. Our approach is that of model reference control, and it extends significantly the capabilities proposed by Prokhorov (2001). We illustrate it with two examples     

On the probabilistic interpretation of neural network classifiersand discriminative training criteria

A probabilistic interpretation is presented for two important issues in neural network based classification, namely the interpretation of discriminative training criteria and the neural network outputs as well as the interpretation of the structure of the neural network. The problem of finding a suitable structure of the neural network can be linked to a number of well established techniques in statistical pattern recognition. Discriminative training of neural network outputs amounts to approximating the class or posterior probabilities of the classical statistical approach. This paper extends these links by introducing and analyzing novel criteria such as maximizing the class probability and minimizing the smoothed error rate. These criteria are defined in the framework of class conditional probability density functions. We show that these criteria can be interpreted in terms of weighted maximum likelihood estimation. In particular, this approach covers widely used techniques such as corrective training, learning vector quantization, and linear discriminant analysis     

高维复杂函数的混合模拟退火全局优化策略

对于高维复杂函数优化问题,经典的优化算法存在着初始点敏感、局部收敛等问题;而模拟退火算法等智能算法则有着计算成本高昂、算法早熟等缺陷。NFL定理犤1犦预示了混合优化策略是解决实际优化问题的最好途径。该文融合了模拟退火算法和经典算法的优点,设计了高维复杂函数混合模拟退火优化策略。混合优化策略具有模拟退火算法的全局收敛性,同时引入强局部收敛经典算法作为模拟退火算法的精英个体提高算子,提高了模拟退火算法局部开采能力,加快了收敛速度。数值仿真计算结果表明,混合模拟退火策略求解高维复杂函数的性能大大优于单一算法,具有强鲁棒性、高收敛速度和高精度等优点。该文的算法设计思想对于解决实际问题有较好的借鉴意义。     

Limit cycle prediction of a neurocontrol vehicle system based on gain-phase margin analysis

Based on some useful frequency domain methods, this paper proposes a systematic procedure to address the limit cycle prediction of a neural vehicle control system with adjustable parameters. A simple neurocontroller can be linearized by using describing function method firstly. According to the classical method of parameter plane, the stability of linearized system with adjustable parameters is then considered. In addition, gain margin and phase margin for limit cycle generation are also analyzed by adding a gain-phase margin tester into open loop system. Computer simulations show the efficiency of this approach.