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Wake distribution prediction on the propeller plane in ship design using artificial intelligence

Ships and Offshore Structures 2006年第2期1卷 89-98页

作者： Kim, S.-Y. Moon, B.Y. Department of Naval Architecture and Ocean Engineering Pusan National University Busan 609-735 30 Jangjeon-Dong Gumjeong-Gu South Korea ILIC (Industrial Liaison Innovation Cluster) Pusan National University Busan 609-735 30 Jangjeon-Dong Gumjeong-Gu South Korea

In this study, a new neuro-fuzzy technique is applied to estimate the wake field distribution on propeller plane of ship. The wake distribution data of stern flow fields have been collected systematically by model tests of ship. When a correlation between geometrical hull information and wake distribution of ship is grasped through the collected data, the obtained correlation can be helpful in designing similar kinds ofships. Characteristics of the wake distribution by the change of stern hull form can be figured out easily by using a neuro-fuzzy system with the collected data. The neuro-fuzzy technique is applied to the ship designing, which is well known as one of the prospective methods for prediction, classification and diagnosis of real complicated problems. To prove the proposed method, three-dimensional stern hull forms and nominal wakevalues from a model test are structured as processing elements of input and output layers, respectively. In order to embody three-dimensional hull forms in a neuro-fuzzy system at the same time to avoid excessive increase in the input data,angle values of certain points of the hull form are used to avoid excessive increase in the input data and simultaneously in order to embody three-dimensional hull form in a neuro-fuzzy system. This method proved satisfactory in obtaining safe predictive results. The results from thisstudy may give advanced information to hull formdesigners for evaluation and optimization of stern hull form by predicting a ship wake distributionat the initial designing stage. © 2006 Taylor and Francis Group, LLC.

关键词： Artificial intelligence Hull form learning algorithm Neural network Neurofuzzy system Optimum design Ship design Wake distribution

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REINFORCEMENT learning CONTROL FOR SHIP STEERING USING RECURSIVE LEAST-SQUARES algorithm

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IFAC Proceedings Volumes 2005年第1期38卷 133-138页

作者： Zhi-peng Shen Chen Guo Shi-chun Yuan Lab of Simulation and Control of Navigation Systems Dalian Maritime Univ. Dalian 116026 China State Key Laboratory of Intelligent Technology and system Tsinghua University Beijing 100084 China

Recursive least-squares temporal difference algorithm (RLS-TD) is deduced, which can use data more efficiently with fast convergence and less computational burden. Reinforcement learning based on recursive least-squares methods is applied to ship steering control, as provides an efficient way for the improvement of ship steering control performance. It removes the defect that the conventional intelligent algorithm learning must be provided with some sample data. The parameters of controller are on-line learned and adjusted. Simulation results show that the ship course can be properly controlled in case of the disturbances of wave, wind, current. It is demonstrated that the proposed algorithm is a promising alternative to conventional autopilots.

关键词： Recursive squares methods learning algorithm Action network Ship control Simulation

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Methods for reducing interference in the Complementary learning Systems model: Oscillating inhibition and autonomous memory rehearsal

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NEURAL NETWORKS 2005年第9期18卷 1212-1228页

作者： Norman, KA Newman, EL Perotte, AJ Princeton Univ Dept Psychol Green Hall Princeton NJ 08544 USA

The stability-plasticity problem (i.e. how the brain incorporates new information into its model of the world, while at the same time preserving existing knowledge) has been at the forefront of computational memory research for several decades. In this paper, we critically evaluate how well the Complementary learning Systems theory of hippocampo-cortical interactions addresses the stability-plasticity problem. We identify two major challenges for the model: Finding a learning algorithm for cortex and hippocampus that enacts selective strengthening of weak memories, and selective punishment of competing memories;and preventing catastrophic forgetting in the case of non-stationary environments (i.e. when items are temporarily removed from the training set). We then discuss potential solutions to these problems: First, we describe a recently developed learning algorithm that leverages neural oscillations to find weak parts of memories (so they can be strengthened) and strong competitors (so they can be punished), and we show how this algorithm outperforms other learning algorithms (CPCA Hebbian learning and Leabra at memorizing overlapping patterns. Second, we describe how autonomous re-activation of memories (separately in cortex and hippocampus) during REM sleep, coupled with the oscillating learning algorithm, can reduce the rate of forgetting of input patterns that are no longer present in the environment. We then present a simple demonstration of how this process can prevent catastrophic interference in an AB-AC learning paradigm. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： hippocampus neocortex neural network interference learning algorithm theta oscillations sleep consolidation

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Generation of fuzzy rules and DNA coding method for cooperative behavior of autonomouse mobile robots(AMRs) - art. no. 60423J

Generation of fuzzy rules and DNA coding method for cooperat...

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3rd International Conference on Mechatronics and Information Technology (ICMIT 05)

作者： Kim, JH Park, JB Yang, HS Park, YP Yonsei Univ Dept Elect & Elect Engn Seoul South Korea

ISBN: (纸本)0819460745

Complex "lifelike" behaviors are composed of local interactions of individuals under fundamental rules of artificial life. In this paper, fundamental rules for cooperative group behaviors, "flocking" and "arrangement" of multiple autonomouse mobile robots were represented by a small number of fuzzy rules by Subtractive clustering algorithm and DNA coding method. Fuzzy rules in Sugeno type and their related parameters were automatically generated from clustering input-output data obtained from the algorithms for the group behaviors. Simulations demonstrate the fuzzy rules successfully realize group intelligence of mobile robots.

关键词： fuzzy rules artificial life artificial intelligence learning algorithm DNA coding method

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Evaluation of blind separated signals using speech recognition system

Evaluation of blind separated signals using speech recogniti...

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International Conference on Computer as a Tool (EUROCON 2005)

作者： Eksler, V Brno Univ Technol Dept Telecommun Fac Elect Engn & Commun Brno 61200 Czech Republic

ISBN: (纸本)142440049X

Output blind separated signals are recovered independent sources obtained from their mixtures using methods of convolutive independent component analysis. These methods have no information about the original sources and the mixing process. For the separation process we adapted and improved a method which is based on the natural gradient approach. The new concept proposed in this paper then tries to measure the hit rate of the source restoration by means of the discrete word intelligibility using an Internet speech recognition system. We are interested in a two-channel case, in which we observe the outputs of a 2 X 2 linear time-invariant system of mixtures of speech, music and noise signals.

关键词： blind signal separation Convolutive ICA independent component analysis microphone array learning algorithm natural gradient speech enhancement speech recognition

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CMAC neural networks and PID concurrent control algorithm and its application to nonlinear system control

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11th Joint International Computer Conference (JICC 2005)

作者： Zhang, MG Liu, MQ Li, WH Lanzhou Univ Technol Sch Elect & Informat Engn Lanzhou 730050 Peoples R China

ISBN: (纸本)9812565329

Cerebellar Model Articulation Controller (CMAC) is a kind of adaptive ANN based on local learning, which has the characters of fast convergence speed, strong generation ability, simple structure. While its advantages over conventional control techniques have been recognized in the literature, it has primarily been in system identification and pattern recognition, but not in control application. A novel adaptive learning algorithm and concurrent control based on CMAC and PID is proposed in this paper. The proposed algorithm and controller are applied to two-order nonlinear system. The experimental and simulated results show that performance of the CMAC based controller using the proposed learning algorithm is more stable and effective than that of the conventional controllers.

关键词： CMAC neural networks PID learning algorithm concurrent control nonlinear system

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Optimization of an evolutionary algorithm for a tactile communication system

Optimization of an evolutionary algorithm for a tactile comm...

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IEEE Congress on Evolutionary Computation

作者： Wilks, C Eckmiller, R Univ Bonn Dept Comp Sci Div Neural Computat D-53117 Bonn Germany

ISBN: (纸本)0780393635

In this paper, we present an optimization method for a learning algorithm for generation of tactile stimuli which are adapted by means of tactile perception of a human. Because of special requirements for tactile perception tuning the optimization of the proposed learning algorithm cannot be performed basing on gradient-descent or likelihood estimation methods. Therefore, an Automatic Tactile Classification (ATC) is introduced for the optimization process. The results show that the ATC equals the tactile comparison of humans and that the learning algorithm is successfully optimized by means of the developed ATC.

关键词： evolutionary computation learning (artificial intelligence) optimisation pattern classification touch (physiological) automatic tactile classification evolutionary algorithms learning algorithm optimization tactile communication system tactile perception

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An EM based training algorithm for cross-language text categorization 05

An EM based training algorithm for cross-language text categ...

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IEEE/WIC/ACM International Conference on Web Intelligence

作者： Rigutini, L Maggini, M Liu, B Univ Siena Dipartimento Ingn Informaz I-53100 Siena Italy

ISBN: (纸本)076952415X

Due to the globalization on the Web, many companies and institutions need to efficiently organize and search repositories containing multilingual documents. The management of these heterogeneous text collections increases the costs significantly because experts of different languages are required to organize these collections. Cross-Language Text Categorization can provide techniques to extend existing automatic classification systems in one language to new languages without requiring additional intervention of human experts. In this paper we propose a learning algorithm based on the EM scheme which can be used to train text classifiers in a multilingual environment. In particular in the proposed approach, we assume that a predefined category set and a collection of labeled training data is available for a given language L-1. A classifier for a different language L-2 is trained by translating the available labeled training set for L-1 to L-2 and by using an additional set of unlabeled documents from L-2. This technique allows us to extract correct statistical properties of the language L-2 which are not completely available in automatically translated examples, because of the different characteristics of language L-1 and of the approximation of the translation process. Our experimental results show that the performance of the proposed method is very promising when applied on a test document set extracted from newsgroups in English and Italian.

关键词： classification expectation-maximisation algorithm learning (artificial intelligence) natural languages text analysis EM based training algorithm cross-language text categorization learning algorithm multilingual documents search repositories

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Computer as thinking agent: Modeling the European power markets using agent-based simulation

Computer as thinking agent: Modeling the European power mark...

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International Conference on Computer as a Tool (EUROCON 2005)

作者： Keseric, N Saguan, M Hadjee, G Vienna Univ Technol Inst Power Syst & Energy Econ Vienna Austria

ISBN: (纸本)142440049X

The ongoing liberalisation of the power sector adds a new dimension to the main issues in modelling of power systems. The very complex interactions and interdependencies among power market participants are much like those studied in game theory. However, the strategies used by market participants are often too complex to be conveniently modelled by standard game theoretic techniques. In addition, there has been much less research in the field of dynamic strategic behaviour and their impact on the electricity price in European markets. In this paper, we show new, prosperous combination of computational science and new ideas in evolutionary economics and cognitive science offering appealing extensions to traditional game theoretical modelling. We demonstrate the feasibility of implementing our approach in Matlab using learning algorithm and illustrate its advantages in more detailed and realistic representation of the strategic behaviour of biggest power producers in European power market.

关键词： agent-based simulation electricity price learning algorithm power markets transmission grid

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Adaptive detecting the time of the object appearance in a sequence of random images

Adaptive detecting the time of the object appearance in a se...

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2nd IASTED International Multi-Conference on Automation, Control, and Information Technology

作者： Salov, GI Russian Acad Sci Siberian Div Inst Computat Math & Math Geophys Novosibirsk 630090 Russia

ISBN: (纸本)0889864616

Two new methods are proposed for the solution of an important class of multidimensional detection problems. The first is extended method developed by the author for adaptive detection of a signal hidden in nose to adaptive detection of an object. This method is based on the approximation for the (unknown) likelihood functional and the learning procedure. The second is application the first method to the problem of quickest detecting the time of the appearance of the object in sequence of independent random images. This method is based on the properties of a sequence of cumulative sums.

关键词： control problem detecting problem learning algorithm sequence of images optimal stopping time

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