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Evolution of an artificial neural network based autonomous land vehicle controller

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1996年第3期26卷 450-463页

作者： Baluja, S School of Computer Science Carnegie Mellon University Pittsburgh PA USA

This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller, The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task, In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail, Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored, Error metrics specific to the task of autonomous vehicle control are introduced;the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented, The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks.

关键词： Artificial neural networks Error correction Evolutionary computation Remotely operated vehicles Land vehicles backpropagation algorithms Algorithm design and analysis Computational efficiency Mobile robots Road vehicles

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TOWARDS THE REALIZATION OF AN ARTIFICIAL TACTILE SYSTEM - FINE-FORM DISCRIMINATION BY A TENSORIAL TACTILE SENSOR ARRAY AND NEURAL INVERSION algorithms

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1995年第6期25卷 933-946页

作者： CAITI, A CANEPA, G DEROSSI, D GERMAGNOLI, F MAGENES, G PARISINI, T UNIV PISA CTR PIAGGIOPISAITALY UNIV PAVIA DEPT COMP & SYST SCIPAVIAITALY

This paper describes techniques and methodologies so far developed to investigate object fine-form discrimination by means of artificial tactile sensors. Sensor arrays, selectively sensitive to stress-tenser components and based on piezoelectric polymer technology, have been realized. Sensor output data are used to solve inverse elastic contact problems, by means of neural networks suitably trained to learn regularized inverse maps. Two possible neural network designs are considered: one is based on the multi-layer perceptron trained with the standard backpropagation algorithm, and the other is based on the use of radial basis functions. In both cases, reconstruction of object shapes is demonstrated to be effective and robust with both simulated and real data.

关键词： Sensor arrays Artificial neural networks Tactile sensors Polymers Neural networks Multi-layer neural network Algorithm design and analysis Multilayer perceptrons backpropagation algorithms Shape

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ROBUST RADAR TARGET CLASSIFIER USING ARTIFICIAL NEURAL NETWORKS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第3期6卷 760-766页

作者： CHAKRABARTI, S BINDAL, N THEAGHARAJAN, K Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA

In this paper an artificial neural network (ANN) based radar target classifier is presented, and its performance is compared with that of a conventional minimum distance classifier. Radar returns from realistic aircraft are synthesized using a thin wire time domain electromagnetic code. The time-varying backscattered electric field from each target is processed using both a conventional scheme and an ANN-based scheme for classification purposes. It is found that a multilayer feedforward ANN, trained using a backpropagation learning algorithm, provides a higher percentage of successful classification than the conventional scheme. The performance of the ANN is found to be particularly attractive in an environment of low signal-to-noise ratio. The performance of both methods are also compared when a preemphasis filter is used to enhance the contributions from the high frequency poles in the target response.

关键词： Robustness Artificial neural networks Airborne radar Aircraft Network synthesis Wire Nonhomogeneous media backpropagation algorithms Signal to noise ratio Filters

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DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE 2024年第1期12卷 138-161页

作者： Wang, Haoyu Li, Xiaofeng Chinese Acad Sci Inst Oceanog Key Lab Ocean Circulat & Waves Qingdao 266071 Peoples R China NOAA Washington DC USA

In the last 40 years, remote sensing technology has evolved, significantly advancing ocean observation and catapulting its data into the big data era. How to efficiently and accurately process and analyze ocean big data and solve practical problems based on ocean big data constitute a great challenge. Artificial intelligence (AI) technology has developed rapidly in recent years. Numerous deep learning (DL) models have emerged, becoming prevalent in big data analysis and practical problem solving. Among these, convolutional neural networks (CNNs) stand as a representative class of DL models and have established themselves as one of the premier solutions in various research areas, including computer vision and remote sensing applications. In this study, we first discuss the model architectures of CNNs and some of their variants as well as how they can be applied to the processing and analysis of ocean remote sensing data. Then, we demonstrate that CNNs can fulfill most of the requirements for ocean remote sensing applications across the following six categories: reconstruction of the 3D ocean field, information extraction, image superresolution, ocean phenomena forecast, transfer learning method, and CNN model interpretability method. Finally, we discuss the technical challenges facing the application of CNN-based ocean remote sensing big data and summarize future research directions.

关键词： Oceans Remote sensing Convolutional neural networks Data models Biological system modeling Computational modeling Ocean temperature Feature extraction Predictive models Transfer learning Spatial resolution Artificial intelligence Big Data Deep learning Image processing Natural language processing backpropagation algorithms

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AN ADAPTIVE LEAST-SQUARES ALGORITHM FOR THE EFFICIENT TRAINING OF ARTIFICIAL NEURAL NETWORKS

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1989年第8期36卷 1092-1101页

作者： KOLLIAS, S ANASTASSIOU, D COLUMBIA UNIV CTR TELECOMMUN NEW YORK NY 10027 USA

A novel learning algorithm is developed for the training of multilayer feedforward neural networks, based on a modification of the Marquardt-Levenberg least-squares optimization method. The algorithm updates the input weights of each neuron in the network in an effective parallel way. An adaptive distributed selection of the convergence rate parameter is presented, using suitable optimization strategies. The algorithm has better convergence properties than the conventional backpropagation learning technique. Its performance is illustrated, using examples from digital image halftoning and logical operations such as the XOR function.< >

关键词： Least squares methods Artificial neural networks Neurons Computer networks Integrated circuit interconnections Fault tolerance Multi-layer neural network Neural networks Feedforward neural networks backpropagation algorithms

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An adaptive neural fuzzy filter and its applications

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1997年第4期27卷 635-656页

作者： Lin, CT Juang, CF Department of Control Engineering National Chiao Tung University Hsinchu Taiwan

A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper, The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules, The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning), In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters, In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error, There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds, However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes, Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible;and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically. Moreover, in contrast to traditional fuzzy systems where the input-output spaces are partitioned as grid type causing the combinatorial growing of fuzzy rules as the input-output dimensions increase, irregular partitions are done in the ANFF according to the distribution of training data so fewer fuzzy rules will be generated, To demonstrate the performance of the ANFF, two applications, the nonlinear channel equalization and the adaptive noise cancellation, are simulated, Efficiency and advantages

关键词： Adaptive filters Nonlinear filters Neural networks backpropagation algorithms Information filtering Information filters Fuzzy systems Signal processing algorithms Fuzzy neural networks Training data

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A novel fuzzy neural network based distance relaying scheme

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IEEE TRANSACTIONS ON POWER DELIVERY 2000年第3期15卷 902-907页

作者： Dash, PK Pradhan, AK Panda, G Reg Engn Coll Dept Elect Engn Rourkela India

This paper presents a nem approach to distance relaying using fuzzy neural network (FNN). The FNN can be viewed either as a fuzzy system, a neural network or fuzzy neural network. The structure is seen as a neural network for training and a fuzzy viewpoint is utilized to gain insight into the system and to simplify the model. The number of rules is determined by the data itself and therefore smaller number of rules is produced. The network is trained with back propagation algorithm. A pruning strategy is applied to eliminate the redundant rules and fuzzification neurons. consequently a compact structure is achieved. The classification and location tasks are accomplished by using different FNN's, Once the fault type is identified by the FNN classifier the selected fault locating FNN estimates the location of the fault accurately, Normalized peaks of fundamental voltage and current waveforms are considered as inputs to all the networks and an additional input derived from df component is fed to fault locating networks. The peaks and de component are extracted from sampled signals by the EKF, Test results show that the nem approach provides robust and accurate classification/location of faults for a variety of power system operating conditions even with resistance in the fault path.

关键词： backpropagation fault location fuzzy neural nets power system analysis computing power system faults power system protection relay protection DC component EKF backpropagation algorithm current waveform distance relaying scheme fault path resistance fault Fault location Power system analysis computing Power system faults backpropagation algorithms back propagation Fuzzy neural networks Relay protection Power system protection DC component

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DIAGONAL RECURRENT NEURAL NETWORKS FOR DYNAMIC-SYSTEMS CONTROL

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第1期6卷 144-156页

作者： KU, CC LEE, KY Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

A new neural paradigm called diagonal recurrent neural network (DRNN) is presented. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer, and the hidden layer is comprised of self-recurrent neurons. Two DRNN's are utilized in a control system, one as an identifier called diagonal recurrent neuroidentifier (DRNI) and the other as a controller called diagonal recurrent neurocontroller (DRNC). A controlled plant is identified by the DRNI, which then provides the sensitivity information of the plant to the DRNC. A generalized dynamic backpropagation algorithm (DBP) is developed and used to train both DRNC and DRNI. Due to the recurrence, the DRNN can capture the dynamic behavior of a system. To guarantee convergence and for faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. Convergence theorems for the adaptive backpropagation algorithms are developed for both DRNI and DRNC. The proposed DRNN paradigm is applied to numerical problems and the simulation results are included.

关键词： Recurrent neural networks Control systems Neural networks backpropagation algorithms Feedforward neural networks Neurons Convergence Adaptive control Dynamic programming Intelligent control

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NETWORKS FOR APPROXIMATION AND LEARNING

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PROCEEDINGS OF THE IEEE 1990年第9期78卷 1481-1497页

作者： POGGIO, T GIROSI, F MIT CTR BIOL INFORMAT PROCCAMBRIDGEMA 02139

The problem of the approximation of nonlinear mapping, (especially continuous mappings) is considered. Regularization theory and a theoretical framework for approximation (based on regularization techniques) that leads to a class of three-layer networks called regularization networks are discussed. Regularization networks are mathematically related to the radial basis functions, mainly used for strict interpolation tasks. Learning as approximation and learning as hypersurface reconstruction are discussed. Two extensions of the regularization approach are presented, along with the approach's corrections to splines, regularization, Bayes formulation, and clustering. The theory of regularization networks is generalized to a formulation that includes task-dependent clustering and dimensionality reduction. Applications of regularization networks are discussed.< >

关键词： Artificial intelligence Neural networks Network synthesis Contracts Approximation methods backpropagation algorithms Network topology Prototypes Associative memory System identification

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Input-output HMM's for sequence processing

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1996年第5期7卷 1231-1249页

作者： Bengio, Y Frasconi, P AT&T BELL LABS HOLMDEL NJ 07733 USA UNIV FLORENCE DIPARTIMENTO SISTEMI & INFORMAT FLORENCE ITALY

We consider problems of sequence processing and propose a solution based on a discrete-state model in order to represent past context, We introduce a recurrent connectionist architecture having a modular structure that associates a subnetwork to each state, The model has a statistical interpretation we call input-output hidden Markov model (IOHMM). It can be trained by the estimation-maximization (EM) or generalized EM (GEM) algorithms, considering state trajectories as missing data, which decouples temporal credit assignment and actual parameter estimation, The model presents similarities to hidden Markov models (HMM's), but allows us to map input sequences to output sequences, using the same processing style as recurrent neural networks. IOHMM's are trained using a more discriminant learning paradigm than HMM's, while potentially taking advantage of the EM algorithm. We demonstrate that IOHMM's are well suited for solving grammatical inference problems on a benchmark problem, Experimental results are presented for the seven Tomita grammars, showing that these adaptive models can attain excellent generalization.

关键词： Hidden Markov models Recurrent neural networks backpropagation algorithms Context modeling Delay State estimation Parameter estimation Inference algorithms Natural languages Production

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