A variable reluctance machine (VRM) has been proven to be an outstanding candidate for electric vehicle (EV) applications. This paper introduces a new double-stator, 12/14/12-pole three-phase hybrid-excited modular variable reluctance machine (MVRM) for EV applications. In order to demonstrate the superiorities of the proposed structure, the static torque characteristics and dynamic performance...

This paper presents a machine learning technique for fault diagnostics in induction motor drives. A normal model and an extensive range of faulted models for the inverter-motor combination were developed and implemented using a generic commercial simulation tool to generate voltages and current signals at a broad range of operating points selected by a machine learning algorithm. A structured n...

Complex systems in a wide variety of areas such as biological modeling, image processing, and language recognition can be modeled using networks of very simple machines called finite automata. A finite automaton is an idealized machine that follows a fixed set of rules and changes its state to recognize a pattern, accepting or rejecting the input based on the state in which it finds itself at t...

In this paper, we focus on outsourcing activities optimization problem in single period setting. In some situations, capacity planning or outsourcing is a one-time event and can be modeled as a single period problem. The aim of this research is to balance the trade-off between two echelons of a supply chain consisting of a single outsourcer and a single subcontractor. Each part is composed of a...

A higher-order single-layer relaxation-type recurrent neural network is employed to plan a task in a decomposed state space. The finite state machine model of the state space of the complex task is decomposed into parallel/series combinations of component machines, each of which represents a subtask, using lattice theoretic techniques. Planning the complex task is realized by identifying the tr...

This paper suggests an approach to neural network training through the simultaneous optimization of architectures and weights with a Particle Swarm Optimization (PSO)-based multiobjective algorithm. Most evolutionary computation-based training methods formulate the problem in a single objective manner by taking a weighted sum of the objectives from which a single neural network model is generat...