Integration of Functional Link Neural Networks into a Parameter Estimation Methodology

Abstract

In the field of robust design, most estimation methods for output responses of input factors are based on the response surface methodology (RSM), which makes several assumptions regarding the input data. However, these assumptions may not consistently hold in real-world industrial problems. Recent studies using artificial neural networks (ANNs) indicate that input–output relationships can be effectively estimated without the assumptions mentioned above. The primary objective of this research is to generate a new, robust design dual-response estimation method based on ANNs. First, a second-order functional-link-NN-based robust design estimation approach has been proposed for the process mean and standard deviation (i.e., the dual-response model). Second, the optimal structure of the proposed network is defined based on the Bayesian information criterion. Finally, the estimated response functions of the proposed functional-link-NN-based estimation method are applied and compared with that obtained using the conventional least squares method (LSM)-based RSM. The numerical example results imply that the proposed functional-link-NN-based dual-response robust design estimation model can provide more effective optimal solutions than the LSM-based RSM, according to the expected quality loss criteria.

Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage

“…The neural models for predicting the temperature recorded during the storage phase of the accumulator were developed in the Matlab environment, using the Neural Network Toolbox add-on. The network's structures consist of an Multi-Layer Perceptron (MLP) [17,[27][28][29][30] with 4 inputs, a diverse number of neurons in the hidden layer (5 or 10 neurons) and 1 neuron in the output layer.…”