This work examines the use of genetic algorithms and neural networks to generate neural network topologies. The data set consists of digital images of objects taken from different angles. A successful neural network topology had been trained on this data, so it was investigated whether the genetic algorithm could evolve a neural network topology capable of learning the training data. The geneti...

In the present paper, informatics-aided quantitative structure activity relationship (QSAR) models using genetic algorithm-partial least square (GA-PLS), genetic algorithm-Kernel partial least square (KPLS), and Levenberg-Marquardt artificial neural network (LM ANN) approach were constructed to access the antimalarial activity (pIC50) of 2,5-diaminobenzophenone derivatives. Comparison of errors...

This thesis describes an attempt to predict the next value in a financial time series using various artificial techniques. The time series in question consists of daily values for commodities futures. First, an artificial neural network is used as a predictor. Then the neural network is augmented with a genetic algorithm. The genetic algorithm first is used to select the parameters for the neur...

recently different approaches have been developed in the field of sensor fault diagnostics based on auto-associative neural network (aann). in this paper we present a novel algorithm called self reconstructing auto-associative neural network (s-aann) which is able to detect and isolate single faulty sensor via reconstruction. we have also extended the algorithm to be applicable in multiple faul...

this paper presents a new observer-based control scheme for a class of nonlinear systems. in the proposed method, nonlinear observer and twisting algorithm controller are employed to realize a sensor-less control strategy for complex systems which makes use of non measurable process information instead of installing as many sensors as possible. due to lack of availability of the complex system ...

This paper addresses control design in networked control system by considering stochastic packet dropouts in the forward path of the control loop. The packet dropouts are modelled by mutually independent stochastic variables satisfying Bernoulli binary distribution. A sliding mode controller is utilized to overcome the adverse influences of stochastic packet dropouts in networked control system...

Prediction of traffic is very crucial for its management. Because of human involvement in the generation of this phenomenon, traffic signal is normally accompanied by noise and high levels of non-stationarity. Therefore, traffic signal prediction as one of the important subjects of study has attracted researchers’ interests. In this study, a combinatorial approach is proposed for traffic signal...

A neural network trading strategy optimized based on Apriori and the genetic algorithm is proposed to determine strategies. Using Neural Network Genetic Algorithm, we find all frequent term sets of fuzzy set mine association rules from set. Enter price dataset, minimum support multiplier; output rule library, iterate.