Removal of the endocrine disrupter butyl benzyl phthalate from the environment

Butyl benzyl phthalate (BBP), an aryl alkyl ester of 1,2-benzene dicarboxylic acid, is extensively used in vinyl tiles and as a plasticizer in PVC in many commonly used products. BBP, which readily leaches from these products, is one of the most important environmental contaminants, and the increased awareness of its adverse effects on human health has led to a dramatic increase in research aimed at removing BBP from the environment via bioremediation. This review highlights recent progress in the degradation of BBP by pure and mixed bacterial cultures, fungi, and in sludge, sediment, and wastewater. Sonochemical degradation, a unique abiotic remediation technique, and photocatalytic degradation are also discussed. The degradation pathways for BBP are described, and future research directions are considered

quot;Application of Al in Reconfigurable Flight Control Systemsquot;

This project develops a reconfiguration methodology for13; aircrafts subjected to actuator/control surface failures. The concept of control mixer has been used to reconfigure the flight control system. The model of the failed aircraft required by the control mixer is estimated using a new intelligent model estimation scheme developed in his work. This model estimation scheme can estimate the model of aircrafts subjected to missing/floating surface, partially missing surface, stuck13; surface types of failures as well as unpredictable failures. A13; representative aircraft AFTI-F16 is considered and cases of stuck13; right elevator, floating right elevator and simultaneous failure13; of left elevator and flap have been investigated. The estimated13; model of the failed aircraft compares well with that of real13; failed aircraft model

A reinforcement learning neural network for adaptive control of Markov chains

In this paper we consider the problem of reinforcement learning in a dynamically changing environment. In this context, we study the problem of adaptive control of finite-state Markov chains with a finite number of controls, The transition and payoff structures are unknown, The objective is to find an optimal policy which maximizes the expected total discounted payoff over the infinite horizon, A stochastic neural network model is suggested for the controller. The parameters of the neural nee, which determine a random control strategy, are updated at each instant using a simple learning scheme, This learning scheme involves estimation of some relevant parameters using an adaptive critic, It is proved that the controller asymptotically chooses an optimal action in each state of the Markov chain with a high probability

Memory Neuron Networks for Identification and Control of Dynamical Systems

This paper discusses memory neuron networks as models for identification and adaptive control of nonlinear dynamical systems. These are a class of recurrent networks obtained by adding trainable temporal elements to feedforward networks that makes the output history-sensitive. By virtue of this capability, these networks can identify dynamical systems without having to be explicitly fed with past inputs and outputs. Thus, they can identify systems whose order is unknown or systems with unknown delay. It is argued that for satisfactory modeling of dynamical systems, neural networks should be endowed with such internal memory. The paper presents a preliminary analysis of the learning algorithm, providing theoretical justification for the identification method. Methods for adaptive control of nonlinear systems using these networks are presented. Through extensive simulations, these models are shown to be effective both for identification and model reference adaptive control of nonlinear systems

Memory neuron networks for identification and control of dynamical systems

Abstract- This paper discusses Memory Neuron Networks as models for identification and adaptive control of nonlinear dy-namical systems. These are a class of recurrent networks obtained by adding trainable temporal elements to feed-forward networks that makes the output history-sensitive. By virtue of this capa-bility, these networks can identify dynamical systems without having to be explicitly fed with past inputs and outputs. Thus, they can identify systems whose order is unknown or systems with unknown delay. It is argued that for satisfactory modeling of dynamical systems, neural networks should be endowed with such internal memory. The paper presents a preliminary analysis of the learning algorithm, providing theoretical justification for the identification method. Methods for adaptive control of nonlinear systems using these networks are presented. Through extensive simulations, these models are shown to be effective both for identification and model reference adaptive control of nonlinear systems. I

Continuous action set learning automata for stochastic Optimization

The problem of optimization with noisy measurements is common in many areas of engineering. The only available information is the noise-corrupted value of the objective function at any chosen point in the parameter space. One well-known method for solving this problem is the stochastic approximation procedure. In this paper we consider an adaptive random search procedure, based on the reinforcement-learning paradigm. The learning model presented here generalizes the traditional model of a learning automaton [Narendra and Thathachar, Learning Automata: An Introduction, Prentice Hall, Englewood Cliffs, 1989]. This procedure requires a lesser number of function evaluations at each step compared to the stochastic approximation. The convergence properties of the algorithm are theoretically investigated. Simulation results are presented to show the efficacy of the learning method