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STORMNET: A connectionist model for dynamic management of wastewater treatment plants during storm events

WATER SCIENCE AND TECHNOLOGY 1996年第1期33卷 247-256页

作者： Gong, N Ding, X Denoeux, T Bertrand-Krajewski, JL Clement, M UNIV TECHNOL COMPIEGNE URA CNRS 817 HEUDIASYC F-60206 COMPIEGNE FRANCE CTIA LYONNAISE EAUX F-33029 BORDEAUX FRANCE

Models for solid transport in sewers during storm events are increasingly used, An important application of these models is the management of treatment plants during storm events so as to improve the quality of receiving waters. However, a major difficulty that prevents more general use of these tools is their calibration, which requires field data, accurate information about catchments and sewers, and a specific methodology. For that reason, a connectionist model called STORMNET has been designed to reproduce and replace usual conceptual and deterministic models. This model requires fewer data, can be automatically calibrated, and is comparatively simple. It is composed of two recurrent neural networks for the simulation of hydrographs and pollutographs of suspended solids, respectively. In this paper, we present an updated version of STORMNET designed for optimal management of wastewater treatment plants during storm events, This model has been validated using both model and real data. The results show the efficiency of STORMNET as a computational tool for simulating stormwater pollution. Copyright (C) 1996 IAWQ.

关键词： hydrological modelling recurrent neural networks solid transfer modelling stormwater pollution training algorithm

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Implementation of a modified Nesterov's Accelerated quasi-Newton Method on Tensorflow 17

Implementation of a modified Nesterov's Accelerated quasi-Ne...

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17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Indrapriyadarsini, S. Mahboubi, Shahrzad Ninomiya, Hiroshi Asai, Hideki Shizuoka Univ Grad Sch Integrated Sci & Technol Hamamatsu Shizuoka Japan Shonan Inst Technol Grad Sch Elect & Informat Engn Fujisawa Kanagawa Japan Shizuoka Univ Res Inst Elect Hamamatsu Shizuoka Japan

ISBN: (纸本)9781538668054

Recent studies incorporate Nesterov's accelerated gradient method for the acceleration of gradient based training. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to drastically improve the convergence speed compared to the conventional quasi-Newton method. This paper implements NAQ for non-convex optimization on Tensorflow. Two modifications have been proposed to the original NAQ algorithm to ensure global convergence and eliminate linesearch. The performance of the proposed algorithm - mNAQ is evaluated on standard non-convex function approximation benchmark problems and microwave circuit modelling problems. The results show that the improved algorithm converges better and faster compared to first order optimizers such as AdaGrad, RMSProp, Adam, and the second order methods such as the quasi-Newton method.

关键词： Neural networks training algorithm quasi-Newton method Nesterov's accelerated gradient Global convergence Tensorflow highly-nonlinear function modeling

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Design and Implementation of A Special Purpose Embedded System for Neural Machine Interface

Design and Implementation of A Special Purpose Embedded Syst...

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IEEE International Conference on Computer Design

作者： Zhang, Xiaorong Huang, He Yang, Qing Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

ISBN: (纸本)9781424489350

Our previous study has shown the potential of using a computer system to accurately decode electromyographic (EMG) signals for neural controlled artificial legs. Because of computation complexity of the training algorithm coupled with real time requirement of controlling artificial legs, traditional embedded systems generally cannot be directly applied to the system. This paper presents a new design of an FPGA-based neural-machine interface for artificial legs. Both the training algorithm and the real time controlling algorithm are implemented on an FPGA. A soft processor built on the FPGA is used to manage hardware components and direct data flows. The implementation and evaluation of this design are based on Altera Stratix II GX EP2SGX90 FPGA device on a PCI Express development board. Our performance evaluations indicate that a speedup of around 280X can be achieved over our previous software implementation with no sacrifice of computation accuracy. The results demonstrate the feasibility of a self-contained, low power, and high performance real-time neural-machine interface for artificial legs.

关键词： FPGA-based neural machine interface PCI express development board artificial limbs computation complexity computer system direct data flow electromyographic signal electromyography embedded systems field programmable gate arrays microprocessor chips neural controlled artificial leg neurophysiology peripheral interfaces soft processor software implementation special purpose embedded system training algorithm

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Neural networks for solid transport modelling in sewer systems during storm events

Neural networks for solid transport modelling in sewer syste...

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IAWQ International Specialized Conference on Sewer Solids - Characteristics, Movement, Effects and Control

作者： Gong, N Denoeux, T Bertrand-Krajewski, JL UNIV TECHNOL COMPIEGNE URA CNRS 817 F-60206 COMPIEGNE FRANCE LAB CENT LYONNAISE EAUX CIRSEE F-78230 LE PECQ FRANCE

Models for solid transport in sewers during storm events are increasingly used by engineers and operators to improve their systems and the quality of receiving waters. However, a major difficulty that prevents more general use of these models is their calibration, which requires field data, accurate information about catchments and sewers, and a specific methodology. Therefore, research has been carried out to assess the ability of connectionist models to reproduce and replace usual models for use by an operator. Such models require fewer data, are self-calibrated, and very easy to use. The first stage presented in this paper consists in a comparison between neural networks and the HYPOCRAS model, using simulations of real pollutographs for single storm events. Two specific recurrent neural networks based on the HYPOCRAS model and a general-purpose recurrent multilayer network are used to simulate hydrographs and pollutographs of TSS. The learning algorithm and the performance criterion used for optimization of these networks are described in detail. Experimental results with simulated and real data are then presented. Copyright (C) 1996 IAWQ. Published by Elsevier Science Ltd.

关键词： hydrological modeling recurrent neural networks solid transfer modeling storm pollution training algorithm

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A speech recognition system using fast learning algorithm and beta wavelet network 15

A speech recognition system using fast learning algorithm an...

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15th International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Ejbali, Ridha Jemai, Olfa Zaied, Mourad Ben Amar, Chokri Natl Sch Engineers Sfax REs Grp Intelligent Machines BP W 3038 Sfax Tunisia

ISBN: (纸本)9781467387095

Speech recognition is a specialized pattern recognition task with several applications such as vocal command system, dictating machines, and understanding systems. In recent years, research on pattern recognition has increased by developing various methods and algorithms for different applications. In this paper, we proposed a novel training algorithm based on the fast Beta wavelet transform for speech recognition. This approach has many advantages compared to other algorithms. The majority of the old approaches need to inverse matrix, which can be computationally intensive. However, the new algorithm is computed by the iterative application of fast wavelet transform to compute connection weights. To highlight our approach, we compared its experimental results to those of the old ones.

关键词： speech recognition pattern recognition fast Beta wavelet network acoustical environment training algorithm

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Implementation and performance results of neural network for power quality event detection

Implementation and performance results of neural network for...

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7th International Symposium on Instrumentation and Control Technology

作者： Huang Weijian Tian Wenzhi Hebei Univ Engn Sch Informat & Elect Engn Handan 056038 Peoples R China Tangshan Coll Tangshan 063000 Peoples R China

ISBN: (纸本)9780819473615

A novel method to detect power quality event in distributed power system combing wavelet network with the improved back-propagation algorithm is presented. The paper tries to explain to design complex supported orthogonal wavelets by compactly supported orthogonal real wavelets, and then explore the extraction of disturbance signal to obtain the feature information, and finally propose several novel wavelet combined information to analyze the disturbance signal, superior to real wavelet analysis result. The feature obtained from WT coefficients are inputted into wavelet network for power quality disturbance pattern recognition. The power quality disturbance recognition model is established and the improved back-propagation algorithm is used to fulfill the network parameter initialization. By means of choosing enough samples to train the recognition model, the type of disturbance can be obtained when signal representing fault is inputted to the trained network. The results of simulation analysis show that the complex wavelet transform combined with wavelet network are more sensitive to signal singularity, and found to be significant improvement over current methods in real-time detection.

关键词： Signal detection power quality event neural network training algorithm power system

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Processing of Redundant Information in Airborne Electronic Systems by means of Neural Networks 39

Processing of Redundant Information in Airborne Electronic S...

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39th IEEE International Conference on Electronics and Nanotechnology (ELNANO)

作者： Sushchenko, O. A. Bezkorovayniy, Y. M. Golytsin, V. O. Natl Aviat Univ Aerosp Control Syst Dept Kiev Ukraine

ISBN: (纸本)9781728120652

Processing of redundant information of the noncollinear measuring instrument developed by the technology of microelectromechanical systems is considered. The algorithm of information processing based on neural networks is proposed. The structural schemes of neural networks for single axis and triaxial inertial sensors are given. The neural network with one hided layer for information processing of noncollinear redundant measuring instrument based on inertial sensors is represented. The features of information processing algorithm are described. A process of a training process is represented. Results of information processing by means of neural networks are given. The obtained results can be useful for improving information used for navigation of moving vehicles of the wide class.

关键词： airborne electronics unmanned aerial vehicles information processing neural network noncollinear measuring instrument training algorithm zero bias

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Study of Double SMO algorithm Based on Attributes Reduction

Study of Double SMO Algorithm Based on Attributes Reduction

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6th International Symposium on Neural Networks

作者： Chen, Chen Hong, Liu Song, Haigang Chen, Xueguang Hou, TieMin Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Peoples R China Bas Res Serv Ministry Sci Beijing 100862 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9783642015090

To solve the classification problem in data mining, this paper proposes double SMO algorithm based on attributes reduction. Firstly attributes reduction deletes irrelevant attributes (or dimensions) to reduce data amount, consequently the total calculation is reduced, the training speed is fastened and Classification mode is easy to understand. Secondly applying SMO algorithm on the sampling dataset to get the approximate separating hyperplane, and then we obtain all the Support vectors of original dataset. Finally again use SMO algorithm on the support vectors to get the final separating hyperplane. It is shown in the experiments that the algorithm reduces the memory space, effectively avoids the noise points' effect on the final separating hyperplane and the precision of the algorithm is better than Decision Tree, Bayesian and Neural Network.

关键词： Data mining Support vector machine Attribute reduction training algorithm

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Improving the operational efficiency of outbound retail logistics using clustering of retailers and consumers

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JOURNAL OF MODELLING IN MANAGEMENT 2018年第3期13卷 646-674页

作者： Mohanty, Mahamaya Singh, Rashmi Shankar, Ravi Indian Inst Technol Delhi Dept Management Studies New Delhi India

Purpose The purpose of this paper is to investigate ways to improve operational efficiency of outbound retail logistics considering retailers and consumers by using clustering approach. The retailers are allocated to serve a cluster of consumers. This study demonstrates economic and environment benefits that are achieved in terms of reduced delivery time, transportation cost and carbon emissions. Design/methodology/approach This study is based on modeling the outbound logistics of a retail chain by using Kohonen self-organizing map (KSOM). KSOM is an unsupervised learning and data analysis method for vector quantization, which is based on Euclidean distance method to form clusters. Findings Appropriate clustering of retailers and consumers provides efficient locations of retailers that are identified using the KSOM training algorithm. It provides optimum distance with lesser delivery time, transportation cost and carbon emissions. Research limitations/implications The implication of research includes modeling of operational procedures in a retail supply chain, which is a crucial task for a business. These operations positively affect the reduction in inventory and distribution costs, improvement in customer service and responsiveness to the ever-changing markets of consumer durables. Overall results are insightful and practical in the sense that implementation would result in consumer convenience, eco-friendly environment, etc. Originality/value There is not enough research available on outbound retail logistics considering retailers and consumers using clustering approach.

关键词： Clustering Operational efficiency Retail logistics Consumer durables Kohenon self-organizing map training algorithm

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WSN Location Method Based on BP Neural Network in NLOS Environment

WSN Location Method Based on BP Neural Network in NLOS Envir...

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International Conference on Wireless Communication and Sensor Network (WCSN)

作者： Yu, Yue Zhang, Ling-hua Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing Jiangsu Peoples R China Jiangsu Prov Engn Res Ctr Telecommun & Network Te Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479970919

Given to the non-line-of-sight(NLOS) error existing in the location of wireless sensor network(WSN), together with the strong anti-noise ability, good data approximation and flexible parallel data processing ability of BP neural network, the method of using BP neural network to optimize the location of WSN nodes is put forward in this paper. Firstly, the source of error is analyzed. Then the traditional BP neural network is optimized on the structure and the algorithm to improve the convergence speed. Finally reliable nodes are used to train the neural network. The trained neural network is applied to fulfill the location of unknown nodes. Meanwhile, the simulation results show that this method can effectively suppress NLOS error and its location precision is superior to the traditional Taylor algorithm and Chan algorithm.

关键词： WSN NLOS BP neural network training algorithm location precision

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