Time series prediction plays an important role in engineering applications. Artificial neural networks seem to be a useful tool to solve these problems. However, in real engineering, the inputs and outputs of many com...
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Time series prediction plays an important role in engineering applications. Artificial neural networks seem to be a useful tool to solve these problems. However, in real engineering, the inputs and outputs of many complicated systems are time-varied functions. Conventional artificial neural networks are not suitable to predicting time series in these systems directly. In order to overcome this limitation, a parallel feedforward process neural network (PFPNN) is proposed. The inputs and outputs of the PFPNN are time-varied functions, which makes it possible to predict time series directly. A corresponding learning algorithm for the PFPNN is developed. To simplify this learning algorithm, appropriate orthogonal basis functions are selected to expand the input functions, output functions and network weight functions. The effectiveness of the PFPNN and its learning algorithm is proved by the Mackey-Glass time series prediction. Finally, the PFPNN is utilized to predict exhaust gas temperature time series in aircraft engine condition monitoring, and the simulation test results also indicate that the PFPNN has a faster convergence speed and higher accuracy than the same scale multilayer feedforward process neural network.
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 informatio...
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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.
The aim of this paper is to introduce a formal language approach to the modelling of electromechanical drives. Modem applications often involve electromechanical drives with high dynamical complexity. The integration ...
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The aim of this paper is to introduce a formal language approach to the modelling of electromechanical drives. Modem applications often involve electromechanical drives with high dynamical complexity. The integration of these systems with information processing techniques allows for new perspectives of modelling their behaviour. The formalism of a formal language-based modelling algorithm is presented and a learning algorithm is proposed for on-line generation of the grammar productions. Grammatical interpolation techniques are also presented to establish new productions by a structural matching procedure. Experimental results are presented and discussed.
A conventional way to enhance the steering features (hence optimize the vehicle dynamics) and steering feeling has been realized with EPS (Electric Power Steering) Systems. Another milestone of steering systems is the...
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A conventional way to enhance the steering features (hence optimize the vehicle dynamics) and steering feeling has been realized with EPS (Electric Power Steering) Systems. Another milestone of steering systems is the steer-by-wire steering system, which has no mechanical connection between the steering wheel and tires. Steer-by-wire systems offer many additional desirable steering characteristics. In this paper, a steer-by-wire implementation with new functions will be introduced. Furthermore first results with real-time experimental examples will be elucidated in detail.
In this paper, a novel hybrid method based on interval-valued fuzzy neural network for approximate of interval-valued fuzzy regression models, is presented. The work of this paper is an expansion of the research of re...
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In this paper, a novel hybrid method based on interval-valued fuzzy neural network for approximate of interval-valued fuzzy regression models, is presented. The work of this paper is an expansion of the research of real fuzzy regression models. In this paper interval-valued fuzzy neural network (IVFNN) can be trained with crisp and interval-valued fuzzy data. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples and compare this method with existing methods.
Active Noise Control (ANC) system is commonly designed and implemented using adaptation algorithm and adaptive control structure. In this paper we present theoretical and experimental result of active noise control sy...
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Active Noise Control (ANC) system is commonly designed and implemented using adaptation algorithm and adaptive control structure. In this paper we present theoretical and experimental result of active noise control system using Recurrent Fuzzy Neural Network (RFNN). RFNN is developed by combining fuzzy logic and neural networks, aimed at producing better control system performance than if we use neural network or fuzzy logic separately. Using a control structure with two multilayer feedforward RFNNs (one RFNN serves as a nonlinear controller while the other one operates as a nonlinear plant model), a recursive least-squares algorithm based on Adjoint Extended Kalman Filter approach is employed for the training of the controller network. Extended Kalman Filter (EKF) algorithm is introduced to develop a new algorithm with faster convergence speed by using nonlinear recursive-least square method. Experimental result using DSP demonstrates effectiveness of the proposed RFNN structure and algorithm to attenuate unwanted noise.
This paper proposes a Fuzzy Inference Net (FIN) method for electricity price zone forecasting. Under smart grid environment, it is important for players to maximize profits and minimize risks though power markets whil...
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This paper proposes a Fuzzy Inference Net (FIN) method for electricity price zone forecasting. Under smart grid environment, it is important for players to maximize profits and minimize risks though power markets while introducing renewable energy into grids. The time series of electricity price becomes more complicated due to the nonlinearity and uncertainties. To capture the behavior of the time series appropriately, more sophisticated methods are required to overcome them as a prediction tool. In this paper, a new method is proposed for price zone forecasting. The proposed method makes use of FIN that evaluates the association probability of unknown data to predetermined clusters with fuzzy inference and self-organization. The selection of input variables is determined by the variable importance of the CART algorithm of data mining. The association probability is used determine which zone the one-step ahead electricity price belong to. The proposed method is tested for real data in comparison with the conventional artificial neural network.
The intensity of the flow accelerated corrosion (FAC) process depends on a great number of parameters with a complicated effect on each other. The use of an intellectual neural network (INN) to solve the FAC predictio...
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The intensity of the flow accelerated corrosion (FAC) process depends on a great number of parameters with a complicated effect on each other. The use of an intellectual neural network (INN) to solve the FAC prediction problem makes it possible to estimate the mutual effects from all the factors involved, to identify the essential properties of the information obtained, and, ultimately, to improve the accuracy of prediction without determining the whole range of dependences among a great deal of factors on which the FAC process depends. An approach is proposed to the creation and training of an optimal neural network for the NPP piping FAC rate prediction problem. Matlab software was used to develop an intellectual neural network to address the problem of the wall thinning prediction for a straight pipe with the VVER NPP single-phase secondary fluid. The network has been trained using an elastic back propagation algorithm, a number of the NS configurations have been studied, and the findings have been analyzed. A conceptual framework has been built for the intellectual system in the form of three NS types: a replicative NS, a Kohonen self-organizing NS, and a back-propagation NS.
Device to device (D2D) communication has attracted enormous attention for future cellular networks which helps to increase the cellular capacity, improve the user throughput, and extend the battery lifetime of user eq...
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Device to device (D2D) communication has attracted enormous attention for future cellular networks which helps to increase the cellular capacity, improve the user throughput, and extend the battery lifetime of user equipments (UEs) by reusing the spectrum resources. However, D2D devices provide interferences in the system while reusing the resources. Proper control of interferences helps to increase the performance of the overall system. Adaptive power allocation among cellular and D2D users contributes to providing an efficient interference management system. In this paper, we propose an online power allocation method, i.e., multi-armed bandit solver for D2D communication. We explore the proposed method to improve the system throughput and D2D throughput as well. We incorporate the set of states for this learning algorithm with the appropriate number of system-defined variables, which increases the observation space and consequently improve the balance of spectrum usage. Finally, we compare our proposed work with existing distributed reinforcement learning and random allocation of resources. Simulation results depict that the proposed resource allocation method outperforms the existing works regarding overall system throughput as well as D2D throughput by efficiently controlling the interference levels.
The paper presents a robust fault diagnosis scheme for detecting and accommodating faults occurring in a class of nonlinear multi-input multi-output dynamical systems. Changes in the system dynamics due to a fault are...
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The paper presents a robust fault diagnosis scheme for detecting and accommodating faults occurring in a class of nonlinear multi-input multi-output dynamical systems. Changes in the system dynamics due to a fault are modeled as nonlinear functions of the control input and state variables. The proposed fault accommodation scheme provides a way to augment the nominal controller such that the stability of the closed-loop system is retained in the presence of an unknown fault and of bounded disturbances. The stability of the robust fault accommodation scheme is rigorously established using Lyapunov redesign methods.
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