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3rd International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM 2013)

作者： Gang, Ding Wei, Wang Xiong Da, Lei Harbin Inst Technol Sch Mechatron Engn Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9783037858882

To predict the aeroengine exhaust gas temperature (EGT) more precisely, a process neuron with time-varying threshold function is proposed in this paper, and then the time-varying threshold process neural network model comprised of the presented process neurons is used for EGT prediction. By introducing a group of appropriate orthogonal basis functions, the input functions, the weight functions and the threshold functions of the time-varying threshold process neural network can be expanded as linear combinations of the given orthogonal basis functions, thus to eliminate the integration operation, then to simplify the time aggregation operation. The corresponding learning algorithm is also presented, and the effectiveness of the time-varying threshold process neural network model is evaluated through the prediction of EGT series from practical aeroengine condition monitoring.

关键词： Exhaust Gas Temperature Prediction Time-Varying Threshold Process Neural Network Time-Varying Threshold Function Orthogonal Basis Function learning algorithm

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Formative Evaluation of Ontology learning Methods for Entity Discovery by Using Existing Ontologies as Reference Standards

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METHODS OF INFORMATION IN MEDICINE 2013年第4期52卷 308-316页

作者： Liu, K. Mitchell, K. J. Chapman, W. W. Savova, G. K. Sioutos, N. Rubin, D. L. Crowley, R. S. Univ Pittsburgh Sch Med Dept Biomed Informat Pittsburgh PA USA Univ Calif San Diego Div Biomed Informat San Diego CA 92103 USA Childrens Hosp Boston MA 02115 USA Harvard Univ Sch Med Boston MA USA Lockheed Martin Corp Fairfax VA USA Stanford Univ Dept Radiol Stanford CA 94305 USA Univ Pittsburgh Sch Med Dept Pathol Pittsburgh PA USA

Objective: Developing a two-step method for formative evaluation of statistical Ontology learning (OL) algorithms that leverages existing biomedical ontologies as reference standards. Methods: In the first step optimum parameters are established. A 'gap list' of entities is generated by finding the set of entities present in a later version of the ontology that are not present in an earlier version of the ontology. A named entity recognition system is used to identify entities in a corpus of biomedical documents that are present in the 'gap list', generating a reference standard. The output of the algorithm (new entity candidates), produced by statistical methods, is subsequently compared against this reference standard. An OL method that performs perfectly will be able to learn all of the terms in this reference standard. Using evaluation metrics and precision-recall curves for different thresholds and parameters, we compute the optimum parameters for each method. In the second step, human judges with expertise in ontology development evaluate each candidate suggested by the algorithm configured with the optimum parameters previously established. These judgments are used to compute two performance metrics developed from our previous work: Entity Suggestion Rate (ESR) and Entity Acceptance Rate (EAR). Results: Using this method, we evaluated two statistical OL methods for OL in two medical domains. For the pathology domain, we obtained 49% ESR, 28% EAR with the Lin method and 52% ESR, 39% EAR with the Church method. For the radiology domain, we obtain 87% ESA, 9% EAR using Lin method and 96% ESR, 16% EAR using Church method. Conclusion: This method is sufficiently general and flexible enough to permit comparison of any OL method for a specific corpus and ontology of interest.

关键词： Ontology learning from text statistical ontology learning method statistical ontology learning algorithm ontology enrichment natural language processing ontology evaluation

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Neuromorphic systems for legged robot control

Neuromorphic systems for legged robot control

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作者： Monteiro, Hugo Alexandre Pereira The University of Edinburgh

学位级别：master

Locomotion automation is a very challenging and complex problem to solve. Besidesthe obvious navigation problems, there are also problems regarding the environmentin which navigation has to be performed. Terrains with obstacles such as rocks, stepsor high inclinations, among others, pose serious difficulties to normal wheeled *** flexibility of legged locomotion is ideal for these types of terrains but thisalternate form of locomotion brings with it its own challenges to be solved, causedby the high number of degrees of freedom inherent to *** problem is usually computationally intensive, so an alternative, using simpleand hardware amenable bio-inspired systems, was studied. The goal of this thesiswas to investigate if using a biologically inspired learning algorithm, integrated in afully biologically inspired system, can improve its performance on irregular terrainby adapting its gait to deal with obstacles in its *** first, two different versions of a learning algorithm based on unsupervised reinforcementlearning were developed and evaluated. These systems worked by correlatingdifferent events and using them to adjust the behaviour of the system so that itpredicts difficult situations and adapts to them beforehand. The difference betweenthese versions was the implementation of a mechanism that allowed for some correlationsto be forgotten and suppressed by stronger ***, a depth from motion system was tested with unsatisfactory results. Thesource of the problems are analysed and discussed. An alternative system based onstereo vision was implemented, together with an obstacle detection system based onneuron and synaptic models. It is shown that this system is able to detect obstaclesin the path of the *** the individual systems were completed, they were integrated together and thesystem performance was evaluated in a series of 3D simulations using various *** simulations allowed to conclude that both learning systems wer

关键词： learning algorithm obstacle detection system Locomotion automation

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K-NN based neuro-fuzzy system for time series prediction

K-NN based neuro-fuzzy system for time series prediction

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14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2013

作者： Wei, Chia-Ching Chen, Thao-Tsen Lee, Shie-Jue Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung 80424 Taiwan

ISBN: (纸本)9780769550053

Neuro-fuzzy systems have been proposed for different applications for many years. In this paper, a k-NN based neuro-fuzzy predictor is developed for time series prediction. We use a neuro-fuzzy system to generate prediction results. A set of fuzzy rules can be generated by a self-constructing clustering method. These rules can be refined by a hybrid learning algorithm. In stead of using all training data to training a model, we utilize the k-NN method to dynamically select k instances for each prediction. Experimental results show that our approach can provide more accurate predictions than other methods. © 2013 IEEE.

关键词： kNN based method learning algorithm Neuro-fuzzy system time series

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On the Solution of Nonlinear Fuzzy Equations System by Fuzzy Neural Networks Method

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2013年第3期15卷 388-393页

作者： Jafarian, Ahmad Nia, S. Measoomy Islamic Azad Univ Urmia Branch Dept Math Orumiyeh Iran

Neural networks have already been successfully applied to model the real world problems. The current research attempts to employ architecture of artificial neural networks for approximating solution of a system of fuzzy equations. For this aim, a multi-layer fuzzified feed-forward neural network (FFNN) on the real connection weights space is used. The proposed neural network architecture is able to approximate the unknowns by using a supervised learning algorithm which is based on the gradient descent method. The given approach has been illustrated by several examples with computer simulations.

关键词： System of fuzzy equations approximate solution fuzzified feed-forward neural network cost function learning algorithm

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Integrating Genetic algorithm into the Development of Computerized Play-Based Assessment Tool for School Readiness

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Procedia - Social and Behavioral Sciences 2013年 82卷 852-857页

作者： Suleiman Iyad Arslan Maha University of Bradford Bradford UK University of Haifa Haifa 32000 Israel

In this paper we introduce the design aspects of the modified genetic algorithm to be integrated into a computerized assessment tool for school readiness. We describe the needed data structures to store the information as a preparation step for the learning procedure. Then we describe the structure of the chromosomes and the algorithms involved in the creation procedure of the chromosome population. We integrate the constraints forced on the system by the specialists’ requirements and we finally devise the formulas to computing the fitness of each chromosome in the population to the user's needs.

关键词： School Readiness Data Mining Special Education Genetic algorithm Cognitive Development School Readiness Assessment Cognitive Assessment Computerized Tool Adaptive Treatment Adaptive algorithm Soci al Network learning algorithm

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Evaluation of Minimal Data Size by Using Entropy, in a HMM Maintenance Manufacturing Use

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IFAC Proceedings Volumes 2013年第9期46卷 1536-1541页

作者： Bernard Roblès Manuel Avila Florent Duculty Pascal Vrignat Stéphane Bégot Frédéric Kratz University of Orleans PRISME Laboratory IRAuS team 2 Avenue Mitterrand 36000 Châteauroux FRANCE PRISME Laboratory IRAuS team ENSI 88 boulevard Lahitolle 18020 Bourges Cedex FRANCE

In this paper, we wish to find a minimal data size in order to better conceptualize industrial maintenance activities. We based our study on data given by a Synthetic Hidden Markov Model. This synthetic model is intended to produce real industrial maintenance observations (or “symbols”), with a corresponding degradation indicator. These time series events are shown as Markov chains, also called “signatures”. The production of symbols is generated by using a uniform and a normal distribution. The evaluation is made by applying Shannon entropy on the HMM parameters. The results show a minimal number of data for each distribution studied. After a discussion about the use of a new “Sliding Window” of symbols usable in a Computerized Maintenance Management System, we developed two industrial applications and compare them with the best optimized “signature” previously found.

关键词： Hidden Markov Models Shannon entropy model optimization learning algorithm predictive maintenance

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Functional data learning by Hilbert feedforward neural networks

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2012年第17期35卷 2111-2121页

作者： Zhao, Jianwei China Jiliang Univ Dept Math Hangzhou 310018 Zhejiang Peoples R China

This paper focuses on learning algorithms for approximating functional data that are chosen from some Hilbert spaces. An effective algorithm, called Hilbert parallel overrelaxation backpropagation (HPORBP) algorithm, is proposed for training the Hilbert feedforward neural networks that are extensions of feedforward neural networks from Euclidean space Rn to some Hilbert spaces. Furthermore, the convergence of the iterative HPORBP algorithm is analyzed, and a deterministic convergence theorem is proposed for the HPORBP algorithm on the basis of the perturbation results of Mangasarian and Solodov. Some experimental results of learning functional data on some Hilbert spaces illustrate the convergence theorem and show that the proposed HPORBP algorithm has a better accuracy than the Hilbert backpropagation algorithm. Copyright (c)?2012 John Wiley & Sons, Ltd.

关键词： functional data feedforward neural network learning algorithm convergence

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Iterative learning Control with Advanced Output Data for an Unknown Number of Non-minimum Phase Zeros

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2012年第8期E95A卷 1416-1419页

作者： Jeong, Gu-Min Moon, Chanwoo Ahn, Hyun-Sik Kookmin Univ Sch Elect Engn Seoul South Korea

This letter investigates an iterative learning control with advanced output data (ADILC) scheme for non-minimum phase (NMP) systems when the number of NMP zeros is unknown. ADILC has a simple learning structure that can be applied to both minimum phase and NMP systems. However, in the latter case, it is assumed that the number of NMP zeros is already known. In this paper, we propose an ADILC scheme in which the number of NMP zeros is unknown. Based on input-to-output mapping, the learning starts from the relative degree. When the input becomes larger than a certain upper bound, we redesign the input update law which consists of the relative degree and the estimated value for the number of NMP zeros.

关键词： iterative learning control non-minimum phase system learning algorithm advanced time

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Training and Application of Process Neural Network Based on Quantum Shuffled Frog Leaping algorithm

Training and Application of Process Neural Network Based on ...

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International Conference on Computer Science and Network Technology

作者： Lijie LIU Qiang ZHANG Coll. Of Inf. Technol. Heilongjiang Bayi Agric. Univ. Daqing China

ISBN: (纸本)9781479905607

Aiming at the problem that it is difficult for BP algorithm to converge because of more parameters in training of process neural networks based on orthogonal basis expansion, a quantum shuffled frog leaping algorithm is presented which combines the quantum theory and is to train the process neural network. In this algorithm, the individuals are expressed with Bloch spherical coordinates of qubits. The quantum individuals are updated by quantum rotation gates, and the mutation of individuals is achieved with Hadamard gates. For the size and direction of rotation angle of quantum rotation gates, a simple determining method is proposed. Above operations extend the search of the solution space effectively. To predict sunspot as an example to validate the presented algorithm.

关键词： backpropagation neural nets quantum computing BP algorithm Bloch spherical coordinates Hadamard gates determining method orthogonal basis expansion process neural network quantum rotation gates quantum shuffled frog leaping algorithm quantum theory qubits Logic gates Neural networks Optimization Quantum computing Sociology Statistics Training learning algorithm Process neural network Quantum Shuffled frog leaping algorithm Neural network quantum computing quanta quantum theory learning algorithms Functional training Sociology Logic gates back propagation Solution space Training quantum gates

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