Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Backpropagation Algorithms for a Broad Class of Dynamic Networks
IEEE Transactions on Neural Networks
Research on development of embedded uninterruptable power supply system for IOT-based mobile service
Computers and Electrical Engineering
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Inferential or soft sensing techniques have been gaining momentum recently as viable alternatives to hardware sensors in various situations, e.g. continuous emission monitoring systems. Dynamic neural networks are used in the present work to develop soft sensors for the NO"x and O"2 emission due to combustion operation in industrial boilers. A simplified structure for the soft sensor is obtained by grouping the input variables, reducing the input data dimension and utilizing the system knowledge. The principal component analysis (PCA) is used to reduce the input data dimension. The genetic algorithm (GA) is used to estimate the system's time delays by optimizing a linear time-delay model. Real data from a boiler plant is used to validate the models. The performance of the proposed dynamic models is compared with static neural network models. The results demonstrate the effectiveness of the proposed models.