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The Fuzzy ARTMAP is a supervised learning method, providing high accuracy in many classifications. In this paper, we explore the role of hardware accelerators in remote sensing classification missions. We focus on the designing and implementing a massively parallel hardware architecture on a field-programmable gate array (FPGA) of the performance phase's algorithm. The implementation is mapped on Xilinx Virtex 6 XC6VLX240T FPGA chip for an embedded system using Xilinx ISE 14.5 software. Embedded blocks dedicated to digital signal processing (DSP) and blocks memories are used. DSP48E1 is part of Virtex-6 FPGAs devices. It boasts of increased capability over previous generations, and is highly customizable, a key feature of this primitive that has motivated and enabled the work presented in this paper. We illustrate the application of this methodology to the valuation of various schemes involving embedded elements. This paper also presents a summary of the performance cost with regard to the speed, power, and required computational resources. [ABSTRACT FROM AUTHOR]

In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management. [ABSTRACT FROM PUBLISHER]

In this paper, the use of extended Kalman filter for the optimization of the parameters of type-2 fuzzy logic systems is proposed. The type-2 fuzzy logic system considered in this study benefits from a novel type-2 fuzzy membership function which has certain values on both ends of the support and the kernel, and uncertain values on other parts of the support. To have a comparison of the extended Kalman filter with other existing methods in the literature, particle swarm optimization and gradient descent-based methods are used. The proposed type-2 fuzzy neuro structure is tested on different noisy input–output data sets, and it is shown that extended Kalman filter has a better performance as compared to the gradient descent-based methods. Although the performance of the proposed method is comparable with the particle swarm optimization method, it is faster and more efficient than the particle swarm optimization method. Moreover, the simulation results show that the proposed novel type-2 fuzzy membership function with the extended Kalman filter has noise rejection property. Kalman filter is also used to train the parameters of type-2 fuzzy logic system in a feedback error learning scheme. Then, it is used to control a real-time laboratory setup ABS and satisfactory results are obtained. [ABSTRACT FROM PUBLISHER]

Type-2 fuzzy logic systems (FLSs) are proposed in the literature as an alternative to type-1 FLSs because of their ability to more effectively model uncertainties that may exist in the rule base. However, the parameters of the system still need to be optimized. For this purpose, the use of a sliding mode control theory-based learning algorithm is proposed in this paper. In the approach, instead of trying to minimize an error function, the parameters of the network are tuned by the proposed algorithm in such a way that the learning error is enforced to satisfy a stable equation. The update rules to achieve this are derived, and the convergence of the parameters is proved by Lyapunov stability method. The performance of the proposed algorithm is tested by simulations on a Duffing oscillator and also by real-time experiments on a laboratory servo system. The results indicate that the given type-2 fuzzy neural network with the proposed learning algorithm can handle the uncertainties in a better way as compared to its type-1 counterpart. Moreover, it is computationally easier to implement in real-time systems. [ABSTRACT FROM AUTHOR]

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system. [ABSTRACT FROM AUTHOR]

This paper proposes an approximation-free adaptive tracking error constraint control scheme for unknown Lagrangian systems. A transformed filtered tracking error surface has been constructed to guarantee the transient and steady-state performance of the position control of the Lagrangian systems combined with adaptive estimation laws for unknown upper bounds of the parameters of the Lagrangian systems. To obtain fast error convergence, a finite-time control has been adopted in the proposed controller. In addition, the proposed controller is effective for the constrained positioning of Lagrangian mechanical systems with unknown deadzone input nonlinearity, without requiring nonlinear approximations or identification processes for unknown parameters of dynamics and deadzone. The effectiveness of the proposed constraint control scheme has been proven by simulation and experimental evaluations for an XY table and an articulated manipulator system in the presence of unknown deadzone. [ABSTRACT FROM AUTHOR]

This paper presents a funnel dynamic surface control combined with fuzzy echo state networks (FESNs) for the prescribed tracking performance of a strict feedback multi-input–multi-output (MIMO) nonlinear dynamic system. A new funnel variable is defined so that the funnel virtual control forces the tracking error to fall within funnel boundary, and adaptive FESN method is also proposed to improve the approximation performance in conventional neural network algorithms. A strict feedback controller and adaptive laws for estimating the uncertainties were derived using the recursive steps of dynamic surface control based on the Lyapunov stability theory. Lyapunov stability analysis confirmed the boundedness and convergence of the closed-loop system. The performance of the proposed control scheme was validated by simulations and experimental applications to the tracking control of a MIMO nonlinear system and a robot manipulator. [ABSTRACT FROM AUTHOR]

Induction motors are subject to different faults which, if undetected, may lead to serious machine failures. From the scrupulous review of related works, it is observed that neuro–fuzzy and neural network (NN)-based fault-detection schemes are performed well for large machines and they are not only expensive but also complex. In this paper, the authors developed the radial-basis-function–multilayer-perceptron cascade-connection NN-based fault-detection scheme for the small and medium sizes of three-phase induction motors. Stator winding interturn short, rotor eccentricity, and both faults simultaneously are selected for demonstration. Simple statistical parameters of stator current are considered as input features. Principal component analysis is used to select suitable inputs to the network. Experimental results are included to show the ability of the proposed classifier for detecting faults. Moreover, the network is tested for the robustness to the uniform and Gaussian noises. Having good classification accuracy with enough robustness to noises, the proposed classifier is suitable for the real-world applications. [ABSTRACT FROM AUTHOR]

In this paper, the model reference zero vibration (MRZV) control design is introduced to improve transient performance of robotic manipulators. MRZV consists of a zero vibration (ZV) shaper as the feedforward control while utilizing model reference control to improve robustness and performance under plant model parameter variations. Performance analysis of the MRZV control is given in terms of residual vibration and it is shown that MRZV can tolerate significantly larger plant model variations while maintaining the performance characteristics compared to standard shaper designs such as ZV and ZV and Derivative. A synthesis procedure is provided for the MRZV method to guarantee a prespecified level of residual vibration. Experimental verifications based on a standard cycle time test are carried out on an Adept Technology Cartesian robot. Results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Describes the dynamic responses of a recurrent-fuzzy-neural-network sliding-mode-controlled permanent-magnet synchronous servo motor. Component of the sliding-mode control system; Application of feedback linearization of nonlinear systems; Concept of incorporating fuzzy logic into a neural network.

A self-organizing fuzzy radial basis-function neural-network controller (SFRBNC) has been proposed to control robotic systems. The SFRBNC uses a radial basis-function neural-network (RBFN) to regulate the parameters of a self-organizing fuzzy controller (SOFC) to appropriate values in real time. This method solves the problem caused by the inappropriate selection of parameters in an SOFC. It also eliminates the dynamic coupling effects between degrees of freedom (DOFs) for robotic system control because the RBFN has coupling weighting regulation capabilities. However, its stability is difficult to demonstrate. To overcome the stability issue, this study developed an adaptive self-organizing fuzzy sliding-mode radial basis-function neural-network controller (ASFSRBNC) for robotic systems. The ASFSRBNC solves the problem of an SFRBNC implementation in determining the stability of the system control. It also applies an adaptive law to modify the fuzzy consequent parameter of a fuzzy logic controller to manipulate a robotic system to improve its control performance. The stability of the ASFSRBNC was proven using the Lyapunov stability theorem. From the experimental results of 6-DOF robotic control tests, the ASFSRBNC achieved better control performance than the SFRBNC as well as the SOFC. [ABSTRACT FROM PUBLISHER]