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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： E.J.M. Arruda Filho G.P.S. Cavalcante Centro Federal de Educação Tecnológica do Pará-CEFET-PA Departamento de Engenharia Elétrica do Centra Tecnológico Universidade Federal do Pará Belem PA Brazil

This work proposes a neural adaptive beamforming system combining a feedforward artificial neural network with a backpropagation learning algorithm and linear, planar and circular antenna arrays. It intends to reduce interfering signals of the type for multi-path, co-channel interference or near-far field problems in communication systems. The system as a whole maximizes the signal/interference rate (SIR) by the orientation control of the radiation pattern of the arrays. The array individual element excitation allows the creation of a radiation pattern orienting the beam in different directions, pointing out a null (or minimum) for the interfering signal, and a maximum for the desired one.

关键词： Adaptive systems Array signal processing Adaptive arrays Linear antenna arrays Antenna radiation patterns Artificial neural networks backpropagation algorithms Antenna feeds Interchannel interference Position control

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Classification of difficult-to-diagnose microcalcifications using fuzzy neural network with convex sets

Classification of difficult-to-diagnose microcalcifications ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： W.M. Grohman A.P. Dhawan Department of Bioengineering University of Tolctlo and Eötvös Lárand University Toledo OH USA

A novel convex set based neuro-fuzzy algorithm for classification of difficult-to-diagnose instances of breast cancer is described. The new approach offers rational advantages over the leading neural algorithm backpropagation. The comparative results obtained using receiver operating characteristic (ROC) analysis show that the ability of the convex set based method to infer knowledge is better than that of backpropagation, making it more suitable for use in real diagnostic systems.

关键词： Fuzzy neural networks backpropagation algorithms Classification algorithms Breast cancer Clustering algorithms Data structures Biomedical engineering Breast biopsy Robustness Neural networks

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Neural networks for sensor fusion in remote sensing

Neural networks for sensor fusion in remote sensing

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Pasika S. Haykin E. Clothiaux R. Stewart McMaster University Hamilton ONT Canada Pennslyvania State University PA USA Environment Canada Downsview ONT Canada

Cloud base height is a continuous variable that falls within the range of zero to fourteen kilometers and is useful for understanding the Earth's radiation budget. Advances in LIDAR (laser radar) technology have provided accurate cloud base height measurements. However, new sensor development and deployment are costly processes. The paper is motivated by a desire to make LIDAR output of cloud base height information available at a network of ground based meteorological stations without actually installing LIDAR sensors. To accomplish this, fifty-seven sensors ranging from multispectral satellite information to standard atmospheric measurements such as temperature and humidity, are fused in what can only be termed as a very complex, non-linear environment. The result is an accurate prediction of cloud base height. Thus, a virtual sensor is created. This fusion is performed via neural network architectures. More specifically the choices of learning algorithms reflect the state-of-the-art in neural network design and include; as local methods, the regularized radial basis function (RBF) network and the support vector machine (SVM). Global methods include the node decoupled extended Kalman filter trained multi-layer perception (NDEKF-MLP), and as a benchmark, the venerable backpropagation algorithm. Overall, the support vector machine has shown itself to be the method of choice especially when complexity was considered, Excessive storage requirements occurred in the RBF case and the global methods required large committee machines to overcome the effects of local minima.

关键词： Neural networks Sensor fusion Remote sensing Laser radar Clouds Support vector machines Temperature sensors backpropagation algorithms Earth Laser fusion

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KORA-2: a new cache replacement policy and its performance

KORA-2: a new cache replacement policy and its performance

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： H. Khalid M.S. Obaidat KFUPM and Motorola Monmouth University USA

In this paper, we propose a new strategy (KORA-2) for the replacement of lines in cache memories. The algorithm is efficient and easily implementable. Trace-driven simulations were performed for 42 different cache configurations using benchmark programs from SPEC92 (Standard performance Evaluation Corporation) benchmark suites. Simulation results illustrate that our algorithm can provide a peak value of approximately 8.71% improvement in the miss ratio over the best performing conventional algorithm (LRU) for the selected benchmark trace files generated from SPEC programs. This translates to a savings of hundreds of thousands of misses for typical programs referencing well over 100 million addresses.

关键词： Neural networks backpropagation algorithms Neurons Cache memory High performance computing History Electronic mail Performance evaluation Fuzzy logic Resource management

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Determining the influence of input parameters on BP neural network output error using sensitivity analysis

Determining the influence of input parameters on BP neural n...

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International Conference on Computational Intelligence and Multimedia Applications

作者： F. Frost V. Karri DCC Offices Comalco Aluminium Limited Bell Bay TAS Australia School of Science and Engineering University of Tasmania Hobart TAS Australia

Neural networks, due to their excellent capabilities for modelling process behaviour, are gaining precedence over traditional empirical modelling techniques, such as statistical methods. While neural networks have good ability to map any reasonable continuous function, they do not easily explain how the inputs are related to an output, and also whether the selected inputs have any significant relationship with an output. There is quite often a need to identify some order of influence of the input variables on the output variable. In this paper, a technique for determining the order of influence of the n elements of the input vector on the m elements of the output vector is presented and discussed. While a sample mathematical function is used to introduce the technique, a more practical application of this method in the aluminium smelting industry is considered. It is shown that, using a sensitivity analysis on the backpropagation (BP) algorithm, the degree of influence of the input parameters on the output error can be successfully estimated.

关键词： Neural networks Sensitivity analysis Input variables Training data Aluminum Australia Sorting Metals industry backpropagation algorithms Manufacturing

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Efficient network training for DOA estimation

Efficient network training for DOA estimation

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kar-Ann Toh Chong-Yee Lee CGIT School of Applied Science Nanyang Technological University Singapore Centre for Wireless Communications Singapore

We treat the estimation of direction of arrival (DOA) in mobile communications as a mapping problem. A multilayer feedforward neural network (FNN) is proposed to establish such a map. The main advantage for a trained FNN is that DOA estimation becomes simple and cost-effective for real-time applications. Since training of FNN by the popular backpropagation algorithm usually requires a large number of training iterations to attain a certain accuracy in terms of network approximation, we propose an efficient network training algorithm based on nonlinear optimization. The FNN is first analyzed to obtain those convex regions containing all local solutions. Then, a search is performed constraining to these convex regions for local minima. Since the search is performed over these convex regions, the proposed algorithm can reduce chances of premature algorithm termination due to low gradient values. Preliminary numerical results are provided to illustrate the potential applications.

关键词： Direction of arrival estimation Neural networks Signal processing algorithms Wireless communication Feedforward neural networks backpropagation algorithms Frequency Base stations Adaptive arrays Antenna arrays

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Error self-recurrent neural networks for control of fast time-varying nonlinear systems

Error self-recurrent neural networks for control of fast tim...

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American Control Conference (ACC)

作者： Chang-goo Lee Sang-min Kim Sung-joong Kim Department of Control & Instrumentation Engineering Chonbuk National University of Korea South Korea

Neural networks and neural network based sliding mode controller are proposed. The neural networks are error self-recurrent neural networks which use a recursive least squares method for fast online learning. The proposed neural networks converge considerably faster than the backpropagation algorithm and have advantages of being less affected by the poor initial weights and learning rate. The controller for a car suspension system is designed according to the sliding mode technique based on on the proposed neural networks. In order to adapt the sliding mode control method each frame distance between ground and vehicle body is estimated and the controller is designed according to estimated neural model.

关键词： Error correction Neural networks Nonlinear control systems Control systems Time varying systems Nonlinear systems Sliding mode control backpropagation algorithms Least squares methods Land vehicles

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Blind equalization using higher order statistics and neural networks: a comparison between multilayer feedforward network and radial basis function network

Blind equalization using higher order statistics and neural ...

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IEEE Signal Processing Workshop on Higher-Order Statistics

作者： Li Rui P. Saratchandran N. Sundararajan School of Electrical & Electronical Engineering Nanyang Technological University Singapore

ISBN: (纸本)0769501400

This paper compares two methods of blind equalization for non-minimum phase systems. Higher order statistics is used to estimate the unknown channel and neural networks are used as equalizers to recover the transmitted signal. Two kinds of neural network equalizers are realized; one is the multilayer feedforward network (MFN) based on the backpropagation algorithm, the other is the minimal resource allocation network (MRAN) which is a radial basis function network. It is shown that the MRAN equalizer performs better and converges faster than MFN equalizer.

关键词： Blind equalizers Higher order statistics Neural networks Multi-layer neural network Radial basis function networks Neurons Feedforward neural networks backpropagation algorithms Digital communication Communication channels

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CARVE - A constructive algorithm for real-valued examples

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1998年第6期9卷 1180-1190页

作者： Young, S Downs, T Univ Oxford Dept Expt Psychol Oxford OX1 3UD England Univ Queensland Dept Elect & Comp Engn Brisbane Qld 4072 Australia

A constructive neural-network algorithm is presented. For any consistent classification task on real-valued training vectors, the algorithm constructs a feedforward network with a single hidden layer of threshold units which implements the task, The algorithm, which we call CARVE, extends the "sequential learning" algorithm of Marchand et al. from Boolean inputs to the real-valued input case, and uses convex hull methods for the determination of the network weights. The algorithm is an efficient training scheme for producing near-minimal network solutions for arbitrary classification tasks. The algorithm is applied to a number of benchmark problems including German and Sejnowski's sonar data, the Monks problems and Fisher's iris data. A significant application of the constructive algorithm is in providing an initial network topology and initial weights for other neural-network training schemes and this is demonstrated by application to backpropagation.

关键词： backpropagation algorithms Pattern classification Sonar applications Iris Network topology Polynomials Neural networks Psychology Search methods Pattern recognition

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Fuzzy classifications using fuzzy inference networks

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1998年第3期28卷 334-347页

作者： Cai, LYL Kwan, HK Varian Associates Inc Ginston Res Ctr Palo Alto CA 94304 USA Univ Windsor Dept Elect Engn Windsor ON N9B 3P4 Canada

In this paper, fuzzy inference models for pattern classifications have been developed and fuzzy inference networks based on these models are proposed. Most of the existing fuzzy rule-based systems have difficulties in deriving inference rules and membership functions directly from training data, Rules and membership functions are obtained from experts. Some approaches use backpropagation (BP) type learning algorithms to learn the parameters of membership functions from training data. However, BP algorithms take a long time to converge and they require an advanced setting of the number of inference rules. The work to determine the number of inference rules demands lots of experiences from the designer. In this paper, self-organizing learning algorithms are proposed for the fuzzy inference networks. In the proposed learning algorithms, the number of inference rules and the membership functions in the inference rules will be automatically determined during the training procedure. The learning speed is fast. The proposed fuzzy inference network (FIN) classifiers possess both the structure and the learning ability of neural networks, and the fuzzy classification ability of fuzzy algorithms. Simulation results on fuzzy classification of two-dimensional data are presented and compared with those of the fuzzy ARTMAP, The proposed fuzzy inference networks perform better than the fuzzy ARTMAP and need less training samples.

关键词： Fuzzy neural networks Partitioning algorithms Inference algorithms Neural networks Pattern recognition Classification algorithms Training data Pattern classification Fuzzy systems backpropagation algorithms

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