The study was carried out with 107 measurements of volumetric soil water content (SWC) and electrical conductivity (EC) for soil profile (0-30 cm) and the estimating accuracy of ordinary kriging (OK) and back-propagation neural network (BPNN) was compared. The results showed that BPNN method predicted a slightly better accurate SWC than that of OK, but differences between both methods were not ...

Face recognition is a field of computer vision that use faces to identify or verify a person. Face recognition used for real time applications and become the most important biometric area. This paper present two methodologies for face recognition. First methodology is feature extraction and dimension reduction using Principal Component Analysis (PCA) technique and second methodology is classifi...

0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.08.144 E-mail address: [email protected] As the wafer size increases, the clustering phenomenon of defects becomes significant. In addition to clustered defects, various clustering patterns also influence thewafer yield. In fact, the recognition of clustering pattern usually exists fuzziness. However, the wafer yield m...

To improve recognition and generalization capability of back-propagation neural networks (BPNN), a hidden layer self-organization inspired by immune algorithm called SONIA, is proposed. B cell construction mechanism of immune algorithm inspires a creation of hidden units having local data recognition ability that improves recognition capability. B cell mutation mechanism inspires a creation of ...

We examine robust optimization problems subject to uncertainty factors in product quality and implicit performance functions, and find the accuracy of the meta-model is crucial to the success of the application of robust optimization of computationally intensive simulation models. We present a new robust optimization methodology based on support vector machine (SVM) and particle swarm algorithm...

This paper proposes a model that predicts the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to Binary Decision Diagrams (BDDs). The BPNN model (BPNNM) is developed through the training process of experimental data already obtained for XOR/XNOR-based Boolean functions. The outcome of this model is a unique matrix for the compl...

BACKGROUND Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such...

In this paper a new procedure for classification of normal and abnormal operating conditions of a process when multiple observation sequences are available is introduced. Signals are converted to discrete observations using the method of triangular representation. For overall classification of the process, the combinatorial method is used to train hidden Markov models when multiple observation ...

Abstract The reliability and safety of lithium-ion batteries (LIBs) are key issues in battery applications. Accurate prediction the state-of-health (SOH) LIBs can reduce or even avoid battery-related accidents. In this paper, a new back-propagation neural network (BPNN) is proposed to predict SOH LIBs. BPNN uses as input LIB voltage, current temperature, well charging time, since it strongly co...

This paper proposes a vehicle detection and tracking system based on forward looking CCD camera. Vehicle tail light location information is employed to generate vehicle candidate, and tail light pair distance is used to adjust vehicle tracking window size. In vehicle detection step, it uses a back propagation neural network (BPNN) which is trained by Gabor feature set. BPNN verifies vehicle can...