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Fast and high performance image subsampling using feedforward neural networks

IEEE TRANSACTIONS ON IMAGE PROCESSING 2000年第4期9卷 720-728页

作者： Dumitras, A Kossentini, F Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

We introduce a fast and high-performance image subsampling method using feedforward artificial neural networks (FANN's). Our method employs a pattern matching technique to extract local edge information within the image, in order to select the FANN desired output values during the supervised training stage. Subjective and objective evaluations of experimental results using still images and video frames show that our method, while less computationally intensive, outperforms the standard lowpass filtering and subsampling method.

关键词： feedforward neural network pattern matching subsampling training algorithm

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Using least square SVM for nonlinear systems modeling and control

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ASIAN JOURNAL OF CONTROL 2007年第2期9卷 215-220页

作者： Zhang, Haoran Wang, Xiaodong Zhang, Changjiang Xu, Xiuling Zhejiang Normal Univ Coll Informat Sci & Technol Jinhua 321004 Peoples R China

Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with good generalization ability. The paper firstly introduces the mathematical model of regression least squares support vector machine (LSSVM), and designs incremental learning algorithms by the calculation formula of block matrix, then uses LSSVM to model nonlinear system, based on which to control nonlinear systems by model predictive method. Simulation experiments indicate that the proposed method provides satisfactory performance, and it achieves superior modeling performance to the conventional method based on neural networks, moreover it achieves well control performance.

关键词： LSSVM training algorithm modeling model predictive control

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Maximum Mutual Information Neural Networks for Hybrid Connectionist-HMM Speech Recognition Systems

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1994年第1期2卷 175-184页

作者： Rigoll, Gerhard Univ Duisburg Dept Comp Sci Fac Elect Engn Duisburg Germany

This paper proposes a novel approach for a hybrid connectionist-hidden Markov model (HMM) speech recognition system based on the use of a neural network as vector quantizer. The neural network is trained with a new learning algorithm offering the following innovations: 1) It is an unsupervised learning algorithm for perceptron-like neural networks that are usually trained in supervised mode. 2) Information theory principles are used as learning criteria, making the network especially suitable for combination with a HMM-based speech recognition system. 3) The neural network is not trained using the standard error-backpropagation algorithm but using instead a newly developed self-organizing learning approach. The use of the hybrid system with the neural vector quantizer results in a 25% error reduction compared with the same HMM system using a standard k-means vector quantizer. The training algorithm can be further refined by using a combination of unsupervised and supervised learning algorithms. Finally, it is demonstrated how the new learning approach can be applied to multiple-feature hybrid speech recognition systems, using a joint information theory-based optimization procedure for the multiple neural codebooks, resulting in a 30% error reduction.

关键词： .error reduction vector quantizer speech recognition system training algorithm new learning Information Neural Networks neural vector

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Quantum inspired PSO for the optimization of simultaneous recurrent neural networks as MIMO learning systems

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NEURAL NETWORKS 2010年第5期23卷 583-586页

作者： Luitel, Bipul Venayagamoorthy, Ganesh Kumar Missouri Univ Sci Technol Real Time Power & Intelligent Syst Lab Rolla MO 65409 USA

training a single simultaneous recurrent neural network (SRN) to learn all outputs of a multiple-input-multiple-output (MIMO) system is a difficult problem. A new training algorithm developed from combined concepts of swarm intelligence and quantum principles is presented. The training algorithm is called particle swarm optimization with quantum infusion (PSO-QI). To improve the effectiveness of learning, a two-step learning approach is introduced in the training. The objective of the learning in the first step is to find the optimal set of weights in the SRN considering all output errors. In the second step, the objective is to maximize the learning of each output dynamics by fine tuning the respective SRN output weights. To demonstrate the effectiveness of the PSO-QI training algorithm and the two-step learning approach, two examples of an SRN learning MIMO systems are presented. The first example is learning a benchmark MIMO system and the second one is the design of a wide area monitoring system for a multimachine power system. From the results, it is observed that SRNs can effectively learn MIMO systems when trained using the PSO-QI algorithm and the two-step learning approach. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： MIMO systems PSO Power system Quantum principles SRN Two-step learning Wide area monitor training algorithm

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Automatic sedimentary microfacies identification from logging curves based on deep process neural network

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第5-Sup期22卷 12451-12457页

作者： Liu, Hui Xu, Shaohua Ge, Xinmin Liu, Jianyu Zahid, Muhammad Aleem Shandong Univ Sci & Technol Coll Min & Safety Engn Qingdao 266590 Shandong Peoples R China China Univ Petr East China Sch Geosci Qingdao 266580 Shandong Peoples R China Lasbela Univ Agr Water & Marine Sci Fac Marine Sci Uthal 90250 Balochistan Pakistan

An automatic sedimentary microfacies identification technique is developed based on the deep process neural network (DPNN), which consist several neurons and general non-time-varying neurons arranged in a certain topological structure. In this technique, the features of the shape and amplitude of logging curves are considered to form the category outputs. Combined with the deep learning theory, the diversity of the process features of logging curves and the complexity of combined features of multiple geophysical logging information are considered, and DPNN is created through the stacked superimposition of deep belief network and BP classifier. The technique maintains the structure and information relevance of process signal data and can characterize the distribution features of logging curves automatically, and classify the process signals directly. The theoretical nature and performance of the improved algorithm is tested and validated by some field examples.

关键词： Sedimentary microfacies identification Well logging data Deep learning Process neural network training algorithm

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Analog VLSI neural network with digital perturbative learning

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING 2002年第5期49卷 359-368页

作者： Koosh, VF Goodman, RM CALTECH Pasadena CA 91125 USA

Two feed-forward neural-network hardware implementations are presented. The first uses analog synapses and neurons with a digital serial weight bus. The chip is trained in loop with the computer performing control and weight updates. By training with the chip in the loop, it is possible to learn around circuit offsets. The second neural network also uses a computer for the global control operations, but all of the local operations are performed on chip. The weights are implemented digitally, and counters are used to adjust them. A parallel perturbative weight update algorithm is used. The chip uses multiple, locally generated, pseudorandom bit streams to perturb all of the weights in parallel. If the perturbation causes the error function to decrease, the weight change is kept;otherwise, it is discarded. Test results from a very large scale integration (VLSI) prototype are shown of both networks successfully learning digital functions such as AND and XOR.

关键词： analog very large scale integration (VLSI) chip-in-loop training algorithm learning neural chips neural network neuromorphic perturbation techniques VLSI feed-forward neural network

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The Hypercube Separation algorithm: A fast and efficient algorithm for on-line handwritten character recognition

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APPLIED INTELLIGENCE 1996年第2期6卷 101-116页

作者： Ulgen, F Akamatsu, N Iwasa, T UNIV TOKUSHIMA TOKUSHIMA 770JAPAN

This paper introduces a new neural network training algorithm, Hypercube Separation (HCS) algorithm which is very fast and guaranteed to learn. HCS is a simple algorithm suitable for hardware implementation which classifies different input patterns presented to it through the formation of multiple hyperplanes. The performance of the HCS algorithm is compared to that of the Binary Synaptic Weights (BSW) algorithm and to the Backpropagation (BP) algorithm in solving the two spiral problem, which is an almost pathological problem for pattern separation. The HCS algorithm was able to successfully separate the input patterns, requiring three orders of magnitude less training time than the BP algorithm and one order of magnitude less hidden layer nodes than the BSW algorithm. We also present the application of HCS to on-line handwritten character recognition with good results, especially when the simple nature of the algorithm is taken into consideration.

关键词： neural network training algorithm character recognition binary weights hyperplane separation

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A radial basis probabilistic process neural network model and corresponding classification algorithm

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APPLIED INTELLIGENCE 2019年第6期49卷 2256-2265页

作者： Liu, Kun Xu, Shaohua Feng, Naidan Shandong Univ Sci & Technol Dept Comp Sci & Engn Qingdao 266590 Shandong Peoples R China

A radial basis probabilistic process neuron (RBPPN) and radial basis probabilistic process neural network (RBPPNN) model are proposed to fuse a priori knowledge for application to time-varying signal pattern classification. RBPPN inputs were multi-channel time-varying signals and a generalized inner product was used to perform spatio-temporal aggregation of input signals in the kernel. Typical signal samples from various pattern subsets in the sample set were used as kernel center functions, which use morphological distribution characteristics and combination relationships to implicitly express prior knowledge for the signal category. The exponential probability function was used as the activation function to achieve kernel transformation and RBPPN probability output. The RBPPNN is composed of process signal input layers, an RBPPN hidden layer, a pattern layer, and a Softmax classifier developed through stacking. Generalized inner product operations were used to conduct probability similarity measurements of distribution characteristics between process signals. The pattern layer selectivity summed inputs from the RBPPN hidden layer to the pattern layer according to the category of the kernel center function. Its outputs were then used as inputs in the Softmax classifier. The proposed RBPPNN information processing mechanism was extended to the time domain, and through learning time-varying signal training samples, achieved extraction, expression, and information association of time-varying signal characteristics, as well as direct classification. It can improve the deficiencies of existing neural networks, such as a complete large-scale training dataset is needed, and the information processing flow is complex. In this paper, the properties of the RBPPNN are analyzed and a specific learning algorithm is presented which synthesizes dynamic time warping, dynamic C-means clustering, and the mean square error algorithm. A series of 12-lead electrocardiogram (ECG) signals

关键词： Dynamic signal classification Radial basis probabilistic process neural network training algorithm ECG signal classification

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Degenerated simplex search method to optimize neural network error function

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KYBERNETES 2013年第1-2期42卷 106-124页

作者： Ahmed, Shamsuddin Univ S Pacific Grad Sch Business Suva Fiji

Purpose - The purpose of this paper is to present a degenerated simplex search method to optimize neural network error function. By repeatedly reflecting and expanding a simplex, the centroid property of the simplex changes the location of the simplex vertices. The proposed algorithm selects the location of the centroid of a simplex as the possible minimum point of an artificial neural network (ANN) error function. The algorithm continually changes the shape of the simplex to move multiple directions in error function space. Each movement of the simplex in search space generates local minimum. Simulating the simplex geometry, the algorithm generates random vertices to train ANN error function. It is easy to solve problems in lower dimension. The algorithm is reliable and locates minimum function value at the early stage of training. It is appropriate for classification, forecasting and optimization problems. Design/methodology/approach - Adding more neurons in ANN structure, the terrain of the error function becomes complex and the Hessian matrix of the error function tends to be positive semi-definite. As a result, derivative based training method faces convergence difficulty. If the error function contains several local minimum or if the error surface is almost flat, then the algorithm faces convergence difficulty. The proposed algorithm is an alternate method in such case. This paper presents a non-degenerate simplex training algorithm. It improves convergence by maintaining irregular shape of the simplex geometry during degenerated stage. A randomized simplex geometry is introduced to maintain irregular contour of a degenerated simplex during training. Findings - Simulation results show that the new search is efficient and improves the function convergence. Classification and statistical time series problems in higher dimensions are solved. Experimental results show that the new algorithm (degenerated simplex algorithm, DSA) works better than the random simplex

关键词： Programming and algorithm theory Neural nets Searching Simplex search Derivative free Artificial neural networks training algorithm Robust Benchmark solution

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Prediction of abrasive wears behavior of dental composites using an artificial neural network

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2023年第6期26卷 710-720页

作者： Suryawanshi, Abhijeet Shivaji Behera, Niranjana VIT Sch Mech Engn SMEC Vellore Tamil Nadu India

Resin composites are widely used as dental restorative materials since dental parts are subjected to prolonged wear and ultimately need to be replaced. The objective of this study is to analyze the potential of the feed-forward back propagation artificial neural network (ANN) in assessing the wear of dental composite materials when immersed in chewable tobacco solution, by utilizing the in-vitro test results of the pin-on-disc tribometer [ASTM G99-04]. In this study, four different dental composite material specimens are dipped in a chewable tobacco solution for a few days, and the specimens are removed from the solution for conducting the wear test. Three different training procedures are used to simulate ANN models for predicting the wear of dental composite specimens. The Bayesian regularization training algorithm outperforms the other algorithms significantly. The findings of the ANN modeling were prominently matching with the results of the experiments;therefore, parametric analysis was used based on the model's predicted values.

关键词： Artificial neural network training algorithm wear dental composite chewable tobacco

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