A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

Efficient Algorithms for Linear Summed Error Structural SVMs

Structural Support Vector Machines (SSVMs) have become a popular tool in machine learning for predicting structured objects like parse trees, Part-of-Speech (POS) label sequences and image segments. Various efficient algorithmic techniques have been proposed for training SSVMs for large datasets. The typical SSVM formulation contains a regularizer term and a composite loss term. The loss term is usually composed of the Linear Maximum Error (LME) associated with the training examples. Other alternatives for the loss term are yet to be explored for SSVMs. We formulate a new SSVM with Linear Summed Error (LSE) loss term and propose efficient algorithms to train the new SSVM formulation using primal cutting-plane method and sequential dual coordinate descent method. Numerical experiments on benchmark datasets demonstrate that the sequential dual coordinate descent method is faster than the cutting-plane method and reaches the steady-state generalization performance faster. It is thus a useful alternative for training SSVMs when linear summed error is used.

Free-Space Interconnect for Optical Switch and Analogue Neural Network

In this paper, the architecture of a vector-matrix multiplier (MVM) is simulated. The optical design can be made compact by the use of GRIN lenses for the optical fan-in. The intended application area was in storage area networks (SANs) but the concept can be applied to a neural network. © 2011 Allerton Press, Inc.

Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

Neural network design for switching network control

A neural network is a highly interconnected set of simple processors. The many connections allow information to travel rapidly through the network, and due to their simplicity, many processors in one network are feasible. Together these properties imply that we can build efficient massively parallel machines using neural networks. The primary problem is how do we specify the interconnections in a neural network. The various approaches developed so far such as outer product, learning algorithm, or energy function suffer from the following deficiencies: long training/ specification times; not guaranteed to work on all inputs; requires full connectivity.

Alternatively we discuss methods of using the topology and constraints of the problems themselves to design the topology and connections of the neural solution. We define several useful circuits-generalizations of the Winner-Take-All circuitthat allows us to incorporate constraints using feedback in a controlled manner. These circuits are proven to be stable, and to only converge on valid states. We use the Hopfield electronic model since this is close to an actual implementation. We also discuss methods for incorporating these circuits into larger systems, neural and nonneural. By exploiting regularities in our definition, we can construct efficient networks. To demonstrate the methods, we look to three problems from communications. We first discuss two applications to problems from circuit switching; finding routes in large multistage switches, and the call rearrangement problem. These show both, how we can use many neurons to build massively parallel machines, and how the Winner-Take-All circuits can simplify our designs.

Next we develop a solution to the contention arbitration problem of high-speed packet switches. We define a useful class of switching networks and then design a neural network to solve the contention arbitration problem for this class. Various aspects of the neural network/switch system are analyzed to measure the queueing performance of this method. Using the basic design, a feasible architecture for a large (1024-input) ATM packet switch is presented. Using the massive parallelism of neural networks, we can consider algorithms that were previously computationally unattainable. These now viable algorithms lead us to new perspectives on switch design.

Uma proposta metodológica de integração de técnicas de análise espectral e de inteligência computacional, baseadas em conhecimento, para o reconhecimento de padrões em imagens multiespectrais

Somente no ano de 2011 foram adquiridos mais de 1.000TB de novos registros digitais de imagem advindos de Sensoriamento Remoto orbital. Tal gama de registros, que possui uma progressão geométrica crescente, é adicionada, anualmente, a incrível e extraordinária massa de dados de imagens orbitais já existentes da superfície da Terra (adquiridos desde a década de 70 do século passado). Esta quantidade maciça de registros, onde a grande maioria sequer foi processada, requer ferramentas computacionais que permitam o reconhecimento automático de padrões de imagem desejados, de modo a permitir a extração dos objetos geográficos e de alvos de interesse, de forma mais rápida e concisa. A proposta de tal reconhecimento ser realizado automaticamente por meio da integração de técnicas de Análise Espectral e de Inteligência Computacional com base no Conhecimento adquirido por especialista em imagem foi implementada na forma de um integrador com base nas técnicas de Redes Neurais Computacionais (ou Artificiais) (através do Mapa de Características Auto- Organizáveis de Kohonen SOFM) e de Lógica Difusa ou Fuzzy (através de Mamdani). Estas foram aplicadas às assinaturas espectrais de cada padrão de interesse, formadas pelos níveis de quantização ou níveis de cinza do respectivo padrão em cada uma das bandas espectrais, de forma que a classificação dos padrões irá depender, de forma indissociável, da correlação das assinaturas espectrais nas seis bandas do sensor, tal qual o trabalho dos especialistas em imagens. Foram utilizadas as bandas 1 a 5 e 7 do satélite LANDSAT-5 para a determinação de cinco classes/alvos de interesse da cobertura e ocupação terrestre em três recortes da área-teste, situados no Estado do Rio de Janeiro (Guaratiba, Mangaratiba e Magé) nesta integração, com confrontação dos resultados obtidos com aqueles derivados da interpretação da especialista em imagens, a qual foi corroborada através de verificação da verdade terrestre. Houve também a comparação dos resultados obtidos no integrador com dois sistemas computacionais comerciais (IDRISI Taiga e ENVI 4.8), no que tange a qualidade da classificação (índice Kappa) e tempo de resposta. O integrador, com classificações híbridas (supervisionadas e não supervisionadas) em sua implementação, provou ser eficaz no reconhecimento automático (não supervisionado) de padrões multiespectrais e no aprendizado destes padrões, pois para cada uma das entradas dos recortes da área-teste, menor foi o aprendizado necessário para sua classificação alcançar um acerto médio final de 87%, frente às classificações da especialista em imagem. A sua eficácia também foi comprovada frente aos sistemas computacionais testados, com índice Kappa médio de 0,86.

Pixelated liquid-crystal light valve for neural network application.

The liquid-crystal light valve (LCLV) is a useful component for performing integration, thresholding, and gain functions in optical neural networks. Integration of the neural activation channels is implemented by pixelation of the LCLV, with use of a structured metallic layer between the photoconductor and the liquid-crystal layer. Measurements are presented for this type of valve, examples of which were prepared for two specific neural network implementations. The valve fabrication and measurement were carried out at the State Optical Institute, St. Petersburg, Russia, and the modeling and system applications were investigated at the Institute of Microtechnology, Neuchâtel, Switzerland.