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The impact of hydrogen refueling station subsidy strategy on China's hydrogen fuel cell vehicle market diffusion

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第35期46卷 18453-18465页

作者： Li, Zhi Wang, Wenju Ye, Meng Liang, Xuedong Sichuan Univ Business Sch Chengdu Peoples R China

Lack of hydrogen refueling stations (HRSs) has hindered the diffusion of hydrogen fuel cell vehicles (HFCVs) in the Chinese transport market. By combining the agent-based model (ABM) and the experience weighted attraction (EWA) learning algorithm, this paper explores the impact of government subsidy strategy for HRSs on the market diffusion of HFCVs. The actions of the parties (government, HRS planning department and consumers) and their interactions are taken into account. The new model suggests dynamic subsidy mode based on EWA algorithm yields better results than static subsidy mode: HFCV purchases, HRS construction effort, total number of HRSs and expected HRS planning department profits all outperform static data by around 27%. In addition, choosing an appropriate initial subsidy strategy can increase the sales of HFCVs by nearly 40%. Early investment from government to establish initial HRSs can also increase market diffusion efficiency by more than 76.7%. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrogen fuel-cell vehicles Hydrogen refueling stations Government subsidy Agent-based model Experience-weighted attraction learning algorithm

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Utilizing feed-back neural network approach for solving linear Fredholm integral equations system

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APPLIED MATHEMATICAL MODELLING 2013年第7期37卷 5027-5038页

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

This paper intended to offer an architecture of artificial neural networks (NNs) for finding approximate solution of a second kind linear Fredholm integral equations system. For this purpose, first we substitute the N-th truncation of the Taylor expansion for unknown functions in the origin system. By applying the suggested neural network for adjusting the real coefficients of given expansions in resulting system. The proposed NN is a two-layer feedback neural network such that it can get a initial vector and then calculates it's corresponding output vector. In continuance, a cost function is defined by using output vector and the target outputs. Consequently, the reported NN using a learning algorithm that based on the gradient descent method, will adjust the coefficients in given Taylor series. Eventually, we have showed this method in comparison with existing numerical methods such as trapezoidal quadrature rule provides solutions with good generalization and high accuracy. The proposed method is illustrated by several examples with computer simulations. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Fredholm integral equation systems Feed-back neural networks (FNN) Cost function Approximate solution learning algorithm

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MEMORY NEURON NETWORKS FOR IDENTIFICATION AND CONTROL OF DYNAMICAL-SYSTEMS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1994年第2期5卷 306-319页

作者： SASTRY, PS SANTHARAM, G UNNIKRISHNAN, KP GM CORP DEPT COMP SCIWARRENMI 48090 UNIV MICHIGAN AI LABANN ARBORMI 48109

This paper discusses Memory Neuron Networks as models for identification and adaptive control of nonlinear dynamical systems. These are a class of recurrent networks obtained by adding trainable temporal elements to feed-forward networks that makes the output history-sensitive. By virtue of this capability, these networks can identify dynamical systems without having to be explicitly fed with past inputs and outputs. Thus, they can identify systems whose order is unknown or systems with unknown delay. It is argued that for satisfactory modeling of dynamical systems, neural networks should be endowed with such internal memory. The paper presents a preliminary analysis of the learning algorithm, providing theoretical justification for the identification method. Methods for adaptive control of nonlinear systems using these networks are presented. Through extensive simulations, these models are shown to be effective both for identification and model reference adaptive control of nonlinear systems.

关键词： .inputs Identify learning algorithm adaptive control recurrent feed-forward Memory Neuron Networks for Identification Control of Dynamical

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Knowledge Transfer Between Different UAVs for Trajectory Tracking

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第3期5卷 4939-4946页

作者： Chen, Zhu Liang, Xiao Zheng, Minghui Univ Buffalo Dept Mech & Aerosp Engn Buffalo NY 14260 USA Univ Buffalo Dept Civil Struct & Environm Engn Buffalo NY 14260 USA

Robots are usually programmed for particular tasks with a considerable amount of hand-crafted tuning work. Whenever a new robot with different dynamics is presented, the well-designed control algorithms for the robot usually have to be retuned to guarantee good performance. It remains challenging to directly program a robot to automatically learn from the experiences gathered by other dynamically different robots. With such a motivation, this letter proposes a learning algorithm that enables a quadrotor unmanned aerial vehicle (UAV) to automatically improve its tracking performance by learning from the tracking errors made by other UAVs with different dynamics. This learning algorithm utilizes the relationship between the dynamics of different UAVs, named the target and training UAVs, respectively. The learning signal is generated by the learning algorithm and then added to the feedforward loop of the target UAV, which does not affect the closed-loop stability. The learning convergence can be guaranteed by the design of a learning filter. With the proposed learning algorithm, the target UAV can improve its tracking performance by learning from the training UAV without baseline controller modifications. Both numerical studies and experimental tests are conducted to validate the effectiveness of the proposed learning algorithm.

关键词： Knowledge transfer learning algorithm heterogeneous systems motion control aerial systems applications

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Artificial Immune Networks: Models and Applications

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2008年第2期1卷 168-176页

作者： Shen, Xian Gao, X. Z. Bie, Rongfang Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China Aalto Univ Dept Elect Engn FI-02150 Espoo Finland

Artificial Immune Systems (AIS), which is inspired by the nature immune system, has been applied for solving complex computational problems in classification, pattern recognition, and optimization. In this paper, the theory of the natural immune system is first briefly introduced. Next, we compare some well-known AIS and their applications. Several representative artificial immune networks models are also discussed. Moreover, we demonstrate the applications of artificial immune networks in various engineering fields.

关键词： natural immune systems artificial immune systems artificial immune networks learning algorithm data mining

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A comprehensive approach for performance evaluation of Indian language identification systems

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第5期43卷 5369-5385页

作者： Deshwal, Deepti Sangwan, Pardeep Dahiya, Naveen Nehra, Neelam Dahiya, Aman Maharaja Surajmal Inst Technol Dept ECE New Delhi India Maharaja Surajmal Inst Technol Dept CSE New Delhi India

Good feature representation is the chief requirement for improving Language Identification (LID) system recognition performance. In this work LID system for Indian languages is proposed based on unsupervised feature learning utilizing Deep Belief Network (DBN). The proposed methodology is implemented in two parts. The first phase of this work is based on extracting MFCC features combined with SDC hybrid features. The resultant hybrid features are further stacked to Deep Belief Network (DBN). The second phase of the proposed work is investigating the performance of various Feed forward back propagation neural network models for classification using different training algorithms. Effect of combining different activation functions and varying the hidden neurons is also investigated The performance of the resultant models is evaluated on the basis of some performance metrics such as the epochs, training time, Mean Square Error, Regression and Mean Absolute Percentage Error. Results indicate that optimal performance is achieved in model trained with Levenberg Marquardt (LM) training algorithm. The activation functions used in the hidden and output layer are "tansig" and "purelin". Similarly, the effect of varying the number of neurons in the hidden layer is not significant in improving the performance of the derived models. FFBPNN models trained with PL and TS activation functions gave best performance indices. Auser defined language database in four different languages Hindi, English, Tamil and Malayalam is used for this work.

关键词： Language identification system deep belief network hybrid features learning algorithm activation function

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Accurate radial wavelet neural-network model for efficient CAD modelling of microstrip discontinuities

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IEE PROCEEDINGS-MICROWAVES ANTENNAS AND PROPAGATION 2000年第4期147卷 277-283页

作者： Harkouss, Y Ngoya, E Rousset, J Argollo, D Fac Sci IRCOM CNRS UMR 6615 F-87060 Limoges France

In the paper, a novel, fast and accurate artificial neural network is proposed for efficient computer-aided design (CAD) modelling of microstrip discontinuities. The authors lay the goundwork: for their investigation of radial-wavelet neural networks RWNN and their application, to determine the scattering parameters of the circuit under study. Wavelet theory may be exploited in deriving a good initialisation for the neural network, and thus improved convergence of the learning algorithm. The problem of finding a good model is then discussed through solutions offered by radial-wavelet networks trained by Broyden-Fletcher-Goldfarb-Shanno (BFGS) and limited memory BFCS (LBFGS) algorithms. Finally, experimental results, which confirm the validity of the RWNN model, are reported.

关键词： scattering parameters waveguide theory electronic engineering computing neural nets limited memory BFGS algorithm convergence accurate radial wavelet neural-network model Waveguide and cavity theory wavelet theory efficient CAD modelling S-parameters Integral transforms in numerical analysis Neural computing techniques Electronic engineering computing microstrip discontinuities Numerical analysis convergence of numerical methods learning algorithm RWNN wavelet transforms Broyden-Fletcher-Goldfarb-Shannon algorithm computer-aided design technology CAD (electronics)

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Dynamic Bayesian modelling of non-stationary stochastic systems using constrained least square estimation and gradient descent optimisation

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IET SIGNAL PROCESSING 2012年第6期6卷 608-615页

作者： Cho, H. Cheol Kim, N. H. Ulsan Coll Sch Elect & Elect Engn Ulsan South Korea Pukyong Natl Univ Dept Control & Instrument Engn Pusan South Korea

A dynamic Bayesian network (DBN) is a statistical tool particularly for representing stochastic casual systems using probability and graph theories. The most important procedure in constructing a DBN is selecting the best parameter vector given as conditional probability distribution through a proper learning algorithm. This study presents a novel parameter learning methodology for Markov chain (MC) and hidden Markov model (HMM) DBN using the constrained least square method and the gradient descent optimisation, respectively. The former is employed for satisfying the probability axiom in an MC model and the latter is applied to derive adjustment rules for HMM parameters. The authors primitively assume that an observation probability vector is necessarily predefined prior to applying of the proposed learning algorithm for both models. Simulation experiment is achieved to test their learning algorithm for modelling non-stationary stochastic systems. The authors additionally provide qualitative comparative study with recently addressed learning methodologies of DBN models.

关键词： hidden Markov model statistical analysis stochastic systems conditional probability distribution parameter learning methodology stochastic casual systems learning (artificial intelligence) Optimisation techniques Markov chain dynamic Bayesian network constrained least square estimation gradient descent optimisation graph theories HMM MC belief networks Combinatorial mathematics learning algorithm Markov processes gradient methods statistical tool least squares approximations learning in AI dynamic Bayesian modelling probability axiom Interpolation and function approximation (numerical analysis) DBN nonstationary stochastic systems

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Simulation and evaluation of system of fuzzy linear Fredholm integro-differential equations with fuzzy neural network

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NEURAL COMPUTING & APPLICATIONS 2019年第8期31卷 3481-3491页

作者： Mosleh, Maryam Otadi, Mahmood Islamic Azad Univ Dept Math Firoozkooh Branch Firoozkooh Iran

In this paper, fuzzy neural network (FNN) can be trained with crisp and fuzzy data. The work of this paper is an expansion of the research of fuzzy linear Fredholm integro-differential equations. In this work, we interpret a fuzzy integro-differential equations. In this paper, a novel hybrid method based on FNN and Newton-Cotes methods with positive coefficient for the solution of system of fuzzy linear Fredholm integro-differential equations of the second kind with fuzzy initial values is presented. The problem formulation of the proposed UA is quite straightforward. To obtain the "best-approximated" solution of system of fuzzy linear Fredholm integro-differential equations, the adjustable parameters of PFNN are systematically adjusted by using the learning algorithm. Within this paper, the fuzzy neural network model is used to obtain an estimate for the fuzzy parameters in a statistical sense. Based on the extension principle, a simple algorithm from the cost function of the fuzzy neural network is proposed, in order to find the approximate parameters. We propose a learning algorithm from the cost function for adjusting of fuzzy weights. Here neural network is considered as a part of a larger field called neural computing or soft computing. Finally, we illustrate our approach by some numerical examples.

关键词： Fuzzy neural networks System of fuzzy linear Fredholm integro-differential learning algorithm

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Variable-Performance Servo System Design Without Actuator Current and Angle Measurement for Rover Vehicles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第11期69卷 12725-12733页

作者： Kim, Seok-Kyoon Ahn, Choon Ki Hanbat Natl Univ Dept Creat Convergence Engn Daejeon 34158 South Korea Korea Univ Sch Elect Engn Seoul 136701 South Korea

This study devises a variable-performance control law for rover vehicles servoing velocity and pitch angle commands. This considers both vehicle and actuator dynamics as well as load and parameter variations. The main merits are summarized as follows. First, the proposed auto-tuning law magnifies the feedback gain for desired systems to achieve transient performance improvement with the convergence property guarantee. Second, the collaboration of pole-zero cancellation controller and disturbance observers eliminates over/undershoots through closed-loop order reduction by a special form of feedback gain. Beneficial closed-loop properties are also derived from the closed-loop analysis. The prototype rover vehicle built with TETRIX and MyRIO-1900 experimentally validates the closed-loop performance and robustness improvement.

关键词： Vehicle dynamics Actuators Angular velocity Wheels Current measurement Acceleration Real-time systems Rover vehicle-tracking control learning algorithm adaptation algorithm active-damping injection technique

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