3 papers
eess.SP2018
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…
eess.SP2018
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…
cs.LG2018
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…