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The paper proposed to apply a fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear plants. The parameters of the local recurrent neural network models are used for a local indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule based control system. The applicability of the proposed...
Generally, selective harmonic elimination (SHE) complexity for multilevel inverters increases with the increase in number of levels. In this paper, a novel approach for SHE is introduced. This approach is based on cancelling the 5th and 7th order harmonics irrelevant of the number of levels in threephase multilevel converters, such that the solution for the angles required for the proposed SHE mimics...
Manoeuvring ship during berthing has always required vast experience, skill and knowledge to provide desired necessary actions. Presence of environmental disturbances as well as decreased manoeuvrability in low speed often makes the whole procedure so sophisticated that even slight mistake may results catastrophic disaster. By knowing the fact that Artificial Neural Network (ANN) has the ability to...
The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users' preferences, wills and needs. However, the users' preferences, wills...
Simulation of various manufacturing processes such as heat treatments is rapidly gaining importance in the industry for process optimization, enhancing efficiency and improving product quality. Case carburization followed by quenching is one such significant heat treatment process commonly used in the automotive industry. The equations to be solved for simulation of these processes are non-linear...
Credit rating prediction using clustering algorithms has become more and more important in the financial literature. Expanding the ideas of [4] and [5], we propose an approach to generate models for automated credit rating prediction based on support vector domain description (SVDD) and linear regression (LR). The models include the prediction for sovereign and corporate bonds. Another advantage is,...
Solar radiation is a source of alternative energy that is very influential on the photovoltaic performance in generating energy. The need for solar radiation estimation has become a significant feature in the design of photovoltaic (PV) systems. Recently, the most popular method used to estimate solar radiation is artificial neural network (ANN). However, a new approach, called the extreme learning...
Classification process is one of the most important operations implemented on the huge data warehouses in order to classify the data. Availability of huge amounts of data increased the need for effective techniques to analyze and classify data accurately. Many algorithms in the field of swarm intelligence are able to contribute to improve the classification accuracy using the optimal algorithm methods...
In this paper, the theme of Exponential Synchronization (ES) for a new class of Complex Dynamical Networks (CDN) with hybrid Time-Varying Delay (TVD) and Non-Time-Varying Delay (NTVD) nodes is investigated by using coupling Periodically Intermittent Pinning Control (PIPC). Based on the Lyapunov Stability Theory (LST), Kronecker product rules and PIPC method, sufficient conditions for ES and PIPC criteria...
In this paper, a novel neural network (NN) adaptive dynamic programming (ADP) control scheme for distributed parameter systems (DPS) governed by parabolic partial differential equations (PDE) is introduced in the presence of control constraints and unknown system dynamics. First, Galerkin method is utilized to develop a relevant reduced order system which captures the dominant dynamics of the DPS...
Customer Classification has important role in Customer Relationship Management (CRM) and has been applied in many industries, such as retail and manufacturing. However, there is no single model purposely created only for telecommunication wholesale segment, especially IP Transit. This research develops a model for customer classification with consideration of all aspects of customer - company relationship...
Three-phase induction motor electric parameter estimation has been widely used to improve induction motor control performance. A precise match between electrical parameter values and estimated ones is imperative. A value deviation can make induction motor misbehave, which can cause motor overheating even instability. Parameter estimation can be achieved on-line or off-line way with a large number...
This paper deals with a 6-bar mechanism, which! finds its application in a precision deep drawing press. The approach for the kinematic simulation is based on loop closure analysis, which has been performed to derive expressions for slider displacement. The results are consolidated using Artificial Neural Network (ANN). Genetic Algorithm (GA) is used for optimizing the dimensions of the mechanism,...
Natural and Intentional Electromagnetic Interference (IEMI) on a network's communications nodes can cause combinations of short-term upset, long-term upset and permanent damage to equipment. In this paper we provide new theoretical principles based on random graph theory and percolation theory to evaluate the resilience of large random geometric ad-hoc networks formed with non-directed and directed...
In this paper, we show how to improve the Radial Basis Function Neural Networks effectiveness by using the Optimum-Path Forest clustering algorithm, since it computes the number of clusters on-the-fly, which can be very interesting for finding the Gaussians that cover the feature space. Some commonly used approaches for this task, such as the well-known fc-means, require the number of classes/clusters...
Nonnegative Boltzmann machines (NNBMs) are recurrent probabilistic neural network models that can describe multi-modal nonnegative data. NNBMs form rectified Gaussian distributions that appear in biological neural network models, positive matrix factorization, nonnegative matrix factorization, and so on. In this paper, an effective inference method for NNBMs is proposed that uses the mean-field method,...
This paper studies the stability problems of Multi-Agent group formation with input constraints. The double-integrator dynamical model with input saturation constraints is established, then the consensus control algorithm is designed. And the condition of how the formation can be built and maintained is introduced. The method of using the diagonal matrix method to handle the input saturation constraints...
A three phases induction motor is used as an electric vehicle propulsion system in this paper. The proposed neural network speed controller is design based on the space vector modulation technique on direct torque control. Since the electric drive performance significantly lean against on the design of speed controller, thus the improvement on the speed controller become the core of this research...
This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control for continuous-time nonlinear systems with unknown system dynamics, which is achieved in terms of a novel identifier-critic based approximate dynamic programming (ADP) structure. To obviate the requirement of complete knowledge of system dynamics, an adaptive neural network (NN) identifier...
This paper deals with stator fault detection of induction motor. Mathematical modeling of induction motor for healthy and stator fault condition are explained. In this paper Artificial Neural Network technique is applied for stator fault detection in induction motor. By collecting the simulation data from the mathematical model developed in MATLAB simulink, ANN is trained. 16 different parameters...
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