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Feedforward neural networks are neural networks with (possibly) multiple layers of neurons such that each layer is fully connected to the next one. They have been widely studied in the past partially due to their universal approximation capabilities and empirical effectiveness on a variety of application domains for both regression and classification tasks. In this paper, we provide an overview on...
To address the high-dimensionality of big data, numerous iterative algorithms have been introduced including least absolute shrinkage selection operator (Lasso) and iteratively sure independent screening (ISIS). However, the iterative nature of these algorithms renders the computational cost of retraining the learning model impractical. We take advantage of this key observation to propose a novel...
We propose a novel approach called, an orthogonal particle swarm optimization (OPSO) algorithm, for economic dispatch (ED) of thermal generating units (TGUs) in smart electric power gird (SEPG) environment. The characteristics of TGUs are nonlinear and the generation system becomes more and more complicated when these TGUs are subjected to ramp rate constraints and prohibited operating zones. In such...
Through multiple levels of abstraction, deep learning takes advantage of multiple layers models to find the complicated structure and learn the high level representations of data. In recent years, deep learning has made great progress in object detection, speech recognition, and many other domains. The robustness of learning systems with deep architectures is however rarely studied and needs further...
Owing to their universal approximation capability and online learning manner, kernel adaptive filters have been widely used in nonlinear systems modeling. Under Gaussian assumption, traditional kernel adaptive algorithms utilize the well-known mean square error(MSE) as a cost function to get optimal solutions. For non-Gaussian situations, MSE will not properly represent the statistics of the error,...
Dynamic neural field (DNF) is a popular mesoscopic model for cortical column interactions. It is widely studied analytically and successfully applied to physiological modelling, bioinspired computation and robotics. DNF behavior emerges from distributed and decentralized interactions between computing units which makes it an interesting candidate as a cellular building-block for unconventional computations...
Stochastic resonance (SR) is a phenomenon occurring in some nonlinear systems by which a signal provided as input that is too small in magnitude to normally influence the system's output can actually influence the system's output once a non-zero level of noise is provided. SR has been extensively studied both theoretically and experimentally, and noise has been exploited for improving the performance...
This paper presents an improvement of the ELMVIS+ method that is proposed for fast nonlinear dimensionality reduction. The ELMVIS++C has an additional supervised learning component compared to ELMVIS+, which is originally an unsupervised method as like the majority of the other dimensionality reduction method. This component prevents samples under the same class being separated apart from each other...
An approximate solution for optimal switching problems with continuous-time dynamics and infinite horizon cost functions is developed in this paper. The proposed solution is a policy iteration-based algorithm and provides a feedback scheduling policy with a negligible real-time computational burden. The convergence to the optimal solution and stability of the system under the proposed solution are...
A novel event-triggered approach for a class of nonlinear continuous-time system is proposed in this paper to reduce the computation cost of the dual heuristic dynamic programming (DHP) algorithm. Two neural networks are included in our design. A critic network is used to estimate the partial derivatives of the cost function with respect to its inputs, and an action network is used to approximate...
Optimal triggering of networked control systems is investigated in this study and the framework of approximate dynamic programming is selected for solving the problem. Different cases including Zero-Order-Hold, Generalized Zero-Order-Hold, and lossy networks are investigated in designing optimal triggering/scheduling laws. After analyzing convergence, optimality, and stability, the performance of...
The problem of optimal switching between different topologies in step-down DC-DC voltage converters is investigated. Different challenges including presence of line and load disturbances as well as constraint on the inductor current are incorporated. The objective is generating the desired voltage with low ripples and high robustness towards the disturbances. The selected tool is a previously developed...
In this paper, we establish a robust optimal control law for a class of continuous-time uncertain nonlinear systems by using a neural-network-based model-free policy iteration approach. The robust control law of the original uncertain nonlinear system is derived by adding a feedback gain to the optimal control law of the nominal system. It is proven that this robust control law can achieve optimality...
The actual cost function for many decision makers (like balancing authorities (BAs) of the power grid) are usually different from the symmetric cost functions that are commonly used to study renewable energy generation forecasts. While the actual cost function may be difficult to obtain, a convex piecewise linear (CPWL) cost function could be used to approximate and model cost in many practical applications...
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