Missouri University of Science and Technology

About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Control theory & Artificial neural network. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.

Male backlash, bargaining, or exposure reduction?: women’s working status and physical spousal violence in India

Abstract: Labor force participation of women is expected to decrease the risk of spousal violence by enhancing their bargaining power or diminishing their contacts with abusive partners. The opposite effect is predicted when female employment induces male backlash. I identify the effect of female employment on spousal violence by exploiting the exogenous variations in rural women’s working status driven by rainfall shocks and the rice–wheat dichotomy. The instrumental variable regression result indicates that female employment significantly reduces the incidence of spousal violence. This result is mainly driven by the exposure reduction effect that dominates male backlash. There is, however, no evidence on the bargaining effect.

Training Recurrent Neural Networks With the Levenberg–Marquardt Algorithm for Optimal Control of a Grid-Connected Converter

Abstract: This paper investigates how to train a recurrent neural network (RNN) using the Levenberg–Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.