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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Duan Pei-yong Li Hui Shandong Jianzhu Univ Sch Elect & Informat Jinan 250101 Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250101 Peoples R China Shandong Jianzhu Univ Sch Thermal Energy Engn Jinan 250101 Peoples R China

ISBN: (纸本)9781424467129

An intelligent inhabited environment applying interconnected embedded agents by network has intelligent reasoning, planning learning, and control capabilities. Thermal and light comforts are two major control objectives for the environment to deal with using data-driven control method. Practically, dynamic association level of agents should be learned from online data with three reasons: changing structure of agents with the devices to be added to or removed from the environment during residents' life, a large number of dimension of input and output vectors making it is very difficult to design learning based controller, and a multitude of interconnected embedded agents resulting in major load in network communication and calculation. This paper presented a novel online learning algorithm to obtain the structure agents with different functions through identifying the associations between inputs and outputs of the environment. An association weight matrix can be calculated online and the embedded agents can be dynamically divided into multiple subgroups. This can reduce dimension of input vector for each subgroup, reducing network communication load among embedded agents, decreasing the complexities of programming, and improving the learning rate of agents. The experiment results demonstrated the effectiveness and significance of the learning algorithm.

关键词： Intelligent environment data driven control multi-agent learning algorithm

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A learning algorithm FOR BOLTZMANN MACHINES

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COGNITIVE SCIENCE 1985年第1期9卷 147-169页

作者： ACKLEY, DH HINTON, GE SEJNOWSKI, TJ JOHNS HOPKINS UNIV DEPT BIOPHYSBALTIMOREMD 21218

The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a prob- lem in o very short time. One kind of computation for which massively porollel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found. Second, there must be some way of choosing internal representations which allow the preexisting hardware connections to be used efficiently for encoding the con- straints in the domain being searched. We describe a generol parallel search method, based on statistical mechanics, and we show how it leads to a gen- eral learning rule for modifying the connection strengths so as to incorporate knowledge obout o task domain in on efficient way. We describe some simple examples in which the learning algorithm creates internal representations thot ore demonstrobly the most efficient way of using the preexisting connec- tivity structure.

关键词： .describe examples allow representations Connections suitable strengths efficiently connectionist hardware learning algorithm satisfaction Carnegie-Mellon

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A learning algorithm to minimize the expectation time of finding a parking place in urban area

A learning algorithm to minimize the expectation time of fin...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Houissa, Asma Barth, Dominique Faul, Nadege Mautor, Thierry Univ Versailles St Quentin En Yvelines 45 Ave Etats Unis F-78530 Versailles France Inst VEDECOM 77 Rue chantiers F-78000 Versailles France

ISBN: (纸本)9781538616291

Urban Parking is a problem that costs time and energy. That is why intelligent parking is a field of research growing very quickly. In a city where no sensor infrastructure within each place is deployed but only a counting system at every intersection is available, we show that it still possible to propose an efficient method that determines an itinerary that minimizes the expected time to find an available parking place. For this, we first model the urban area by a graph. Then, we implement a learning algorithm that uses a reinforcement learning method. In this model, each agent modeling an intersection, learns the best next street portion. At each step, all the decisions taken by the agents generate an itinerary whose expectation time is the basis for updating the parameters of learning. The execution times and performances of the learning algorithm are compared with those of a method that constructs step by step the itinerary by choosing the next segment with an evaluation of the future expectation time within this segment. We evaluate the performance of the learning algorithm by realistic simulations. The simulation data are extracted from the map of Versailles.

关键词： Smart parking learning algorithm

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A novel learning algorithm which makes multilayer neural networks multiple-weight-fault tolerant

A novel learning algorithm which makes multilayer neural net...

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9th Pacific Rim International Symposium on Dependable Computing

作者： Takanami, I Oyama, Y Ichinoseki Natl Coll Technol Ichinoseki 0218511 Japan Digital Culture Technol Co Morioka Iwate 0200021 Japan

We propose an efficient algorithm for making multi-layered neural networks (MLN) fault-tolerant to all multiple weight faults in a multi-dimensional interval by injecting intentionally two extreme multi-dimensional values in the interval into the weights of the selected multiple links in a learning phase. The degree of fault-tolerance to a multiple weight fault is measured by the number of essential multiple links. First, we analytically discuss how to choose effectively the multiple links to be injected, and present a learning algorithm for making MLNs fault tolerant to all multiple (i.e., simultaneous) faults in the interval defined by two multi-dimensional extreme points. Then it is proved that after the learning algorithm successfully finishes, MLNs become fault tolerant to all multiple faults in the interval. It is also shown that the time in a weight modification cycle depends little on multiplicity of faults k for small k. These are confirmed by simulation.

关键词： multilayer neural network fault tolerance multiple weight fault fault injection learning algorithm

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Design of RBF neural network using an efficient Hybrid learning algorithm with application in human face recognition with Pseudo Zernike Moment

Design of RBF neural network using an efficient Hybrid Learn...

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LA Symposium - Languages and Automata Theories

作者： Haddadnia, J Faez, K Ahmadi, M Moallem, P Amirkabir Univ Technol Dept Elect Engn Tehran 15914 Iran Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

This paper presents an efficient Hybrid learning algorithm (HLA) for Radial Basis Function Neural Network (RBFNN). The HLA combines the gradient method and the linear least squared method for adjusting the RBF parameters and connection weights. The number of hidden neurons and their characteristics are determined using an unsupervised clustering procedure, and are used as input parameters to the learning algorithm. We demonstrate that the HLA, while providing faster convergence in training phase, is also less sensitive to training and testing patterns. The proposed HLA in conjunction with RBFNN is used as a classifier in a face recognition system to show the usefulness of the learning algorithm. The inputs to the RBFNN are the feature vectors obtained by combining shape information and Pseudo Zernike Moment (PZM). Simulation results on the Olivetti Research Laboratory (ORL) database and comparison with other algorithms indicate that the HLA yields excellent recognition rate with less hidden neurons in human face recognition.

关键词： RBF neural network pattern recognition face recognition learning algorithm

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Pocket Estimator - A commercial solution to provide free parametric software estimation combining an expert and a learning algorithm

Pocket Estimator - A commercial solution to provide free par...

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38th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA) / 15th EUROMICRO Digital Systems Design (DSD)

作者： Schnitzhofer, Florian Schnitzhofer, Peter ReqPOOL GmbH Hagenberg Austria Johannes Kepler Univ Linz Linz Austria

ISBN: (纸本)9780769547909

Pocket Estimator is a cloud-based framework to combine an expert weighted estimation algorithm with several learning algorithms for high level, parametric software effort estimation. Main goal of our framework is to create a huge estimation dataset of software implementation projects. This database will be built over the next 2 years and should be used for further scientific research in learning and adjusted effort estimation. We have implemented a k-nearest-neighbor and an expert weighted estimation algorithm. This paper presents our framework and describes the interaction of the parametric software estimation algorithms.

关键词： effort estimation of software development projects learning algorithm k-nearest-neighbour estimation framework mobile estimation device

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Visualizing Output for a Data learning algorithm

Visualizing Output for a Data Learning Algorithm

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Conference on Cyber Sensing

作者： Carson, Daniel Graham, James Ternovskiy, Igor Riverside Res Beavercreek OH 45431 USA Ohio Univ Athens OH 45701 USA Air Force Res Lab Sensors Directorate Wright Patterson AFB OH 45324 USA

ISBN: (纸本)9781510600676

This paper details the process we went through to visualize the output for our data learning algorithm. We have been developing a hierarchical self-structuring learning algorithm based around the general principles of the LaRue model. One example of a proposed application of this algorithm would be traffic analysis, chosen because it is conceptually easy to follow and there is a significant amount of already existing data and related research material with which to work with. While we choose the tracking of vehicles for our initial approach, it is by no means the only target of our algorithm. Flexibility is the end goal, however, we still need somewhere to start. To that end, this paper details our creation of the visualization GUI for our algorithm, the features we included and the initial results we obtained from our algorithm running a few of the traffic based scenarios we designed.

关键词： learning algorithm autonomous learning sense-making data modeling data visualization

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Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

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International journal of neural systems 2004年第5期14卷 313-323页

作者： Jankovic, Marko Ogawa, Hidemitsu Control Department Electrical Engineering Institute Nikola Tesla Koste Glavinica 8a Belgrade 11000

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace learning algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

关键词： Feature extraction learning algorithm neural networks time hierarchy

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Multiresolution Process Neural Network and Its learning algorithm 4

Multiresolution Process Neural Network and Its Learning Algo...

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4th International Conference on Systems and Informatics (ICSAI)

作者： Li, Yang An, Yi Yu, Ning Zhu, Rui-bo State Grid Informat & Telecommun Co SEPC 71 Fu Dong St Taiyuan 030001 Shanxi Peoples R China

ISBN: (纸本)9781538611074

A new model of multiresolution process neural network (MRPNN) which incorporates the characteristics of hierarchical, multiresolution and local learning capability is proposed based on the multiresolution analysis theory and process neural network model. This type of neural network facilitates in tackling with continuous input signals, which makes it possible to forecast time series problem. In addition, in order to approximate the nonlinear system, the hidden layer is used to deal with the nonlinear and complexity problems. A novel learning algorithm is given to expand the input functions and network weight functions based on the expansion of the orthogonal basis functions, subsequently The learning algorithm then builds the network by locating high error regions and adding nodes that get its activation function from the higher resolution space of the current local node, and its support falls within the high error region. Finally, the network is used to forecast the medium-term load of power system. Simulation results show that the network has good convergence and high accuracy. This method provides an effective solution to medium-term load forecasting in power system.

关键词： Process neuron Multiresolution process neural network (MRPNN) learning algorithm Power load forecasting

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A learning algorithm for fault tolerant feedforward neural networks

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1997年第1期E80D卷 21-27页

作者： Hammadi, C Ito, H CHIBA UNIV FAC ENGN CHIBA 263 JAPAN

A new learning algorithm is proposed to enhance fault tolerance ability of the feedforward neural networks. The algorithm focuses on;he links (weights) that may cause errors at the output when they are open faults. The relevances of the synaptic weights to the output error (i.e. the sensitivity of the output error to the weight fault) are estimated in each training cycle of the standard backpropagation using the Taylor expansion of the output around fault-free weights. Then the weight giving the maximum relevance is decreased. The approach taken by the algorithm described in this paper is to prevent the weights from having large relevances. The simulation results indicate that the network trained with the proposed algorithm do have significantly better fault tolerance than the network trained with the standard backpropagation algorithm. The simulation results show that the fault tolerance and the generalization abilities are improved.

关键词： feedforward neural network learning algorithm relevance of synaptic weights essential link open faults

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