6 papers
Anomaly Classification in Distribution Networks Using a Quotient Gradient System
Hamid Khodabandehlou, Iman Niazazari, Hanif Livani +1
The classification of anomalies or sudden changes in power networks versus normal abrupt changes or switching actions is essential to take appropriate maintenance actions that guar…
Networked Model Predictive Control Using a Wavelet Neural Network
H. Khodabandehlou, M. Sami Fadali
In this study, we use a wavelet neural network with a feedforward component and a model predictive controller for online nonlinear system identification over a communication networ…
Training Recurrent Neural Networks via Dynamical Trajectory-Based Optimization
Hamid Khodabandehlou, M. Sami Fadali
This paper introduces a new method to train recurrent neural networks using dynamical trajectory-based optimization. The optimization method utilizes a projected gradient system (P…
Nonlinear System Identification using Neural Networks and Trajectory-Based Optimization
Hamid Khodabandehlou, Mohammed Sami Fadali
In this paper, we study the identification of two challenging benchmark problems using neural networks. Two different global optimization approaches are used to train a recurrent n…
Training Recurrent Neural Networks as a Constraint Satisfaction Problem
Hamid Khodabandehlou, M. Sami Fadali
This paper presents a new approach for training artificial neural networks using techniques for solving the constraint satisfaction problem (CSP). The quotient gradient system (QGS…
Adaptive Matching Pursuit based Online Identification and Control Scheme for Nonlinear Systems
Hamid Khodabandehlou
The complexity of adaptive control of nonlinear time-varying systems requires the use of novel methods that have lower computational complexity as well as ensuring good performance…