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Article Citation - WoS: 1Citation - Scopus: 2Potential of Support-Vector Regression for Forecasting Stream Flow(Univ Osijek, Tech Fac, 2014) Radzi, Mohd Rashid Bin Mohd; Shamshirband, Shahaboddin; Aghabozorgi, Saeed; Misra, Sanjay; Akib, Shatirah; Kiah, Laiha Mat; Computer EngineeringStream flow is an important input for hydrology studies because it determines the water variability and magnitude of a river. Water resources engineering always deals with historical data and tries to estimate the forecasting records in order to give a better prediction for any water resources applications, such as designing the water potential of hydroelectric dams, estimating low flow, and maintaining the water supply. This paper presents three soft-computing approaches for dealing with these issues, i.e. artificial neural networks (ANNs), adaptive-neuro-fuzzy inference systems (ANFISs), and support vector machines (SVMs). Telom River, located in the Cameron Highlands district of Pahang, Malaysia, was used in making the estimation. The Telom River's daily mean discharge records, such as rainfall and river-level data, were used for the period of March 1984-January 2013 for training, testing, and validating the selected models. The SVM approach provided better results than ANFIS and ANNs in estimating the daily mean fluctuation of the stream's flow.Letter Citation - WoS: 3Citation - Scopus: 3Global Robust Stability of Interval Delayed Neural Networks: Modified Approach(Wiley, 2009) Singh, VimalA criterion for the global robust stability of Hopfield-type delayed neural networks with the intervalized network parameters is presented. The criterion, which is derived by utilizing the idea of splitting the given interval into two intervals, is in the form of linear matrix inequality and, hence, computationally tractable. The criterion yields a less conservative condition compared with many recently reported criteria, as is demonstrated with an example. Copyright (C) 2008 John Wiley & Sons, Ltd.Article Citation - WoS: 44Citation - Scopus: 49A Neural Network-Based Approach for Calculating Dissolved Oxygen Profiles in Reservoirs(Springer London Ltd, 2003) Soyupak, S; Karaer, F; Gürbüz, H; Kivrak, E; Sentürk, E; Yazici, AA Neural Network (NN) modelling approach has been shown to be successful in calculating pseudo steady state time and space dependent Dissolved Oxygen (DO) concentrations in three separate reservoirs with different characteristics using limited number of input variables. The Levenberg-Marquardt algorithm was adopted during training. Pre-processing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The correlation coefficients between neural network estimates and field measurements were as high as 0.98 for two of the reservoirs with experiments that involve double layer neural network structure with 30 neurons within each hidden layer. A simple one layer neural network structure with 11 neurons has yielded comparable and satisfactorily high correlation coefficients for complete data set, and training, validation and test sets of the third reservoir.Article Citation - WoS: 42Citation - Scopus: 47A Novel Global Robust Stability Criterion for Neural Networks With Delay(Elsevier Science Bv, 2005) Singh, VA criterion based on the intervalised network parameters for the global robust stability of Hopfield-type neural networks with delay is presented. The criterion is compared with an earlier criterion. (c) 2005 Elsevier B.V. All rights reserved.Article Citation - WoS: 13Citation - Scopus: 14Improved Global Robust Stability for Interval-Delayed Hopfield Neural Networks(Springer, 2008) Singh, VimalA modified form of a recent criterion for the global robust stability of interval-delayed Hopfield neural networks is presented. The effectiveness of the modified criterion is demonstrated with the help of an example.Article Citation - WoS: 77Citation - Scopus: 101An Intelligent Process Planning System for Prismatic Parts Using Step Features(Springer London Ltd, 2007) Amaitik, Saleh M.; Kilic, S. EnginThis paper presents an intelligent process planning system using STEP features (ST-FeatCAPP) for prismatic parts. The system maps a STEP AP224 XML data file, without using a complex feature recognition process, and produces the corresponding machining operations to generate the process plan and corresponding STEP-NC in XML format. It carries out several stages of process planning such as operations selection, tool selection, machining parameters determination, machine tools selection and setup planning. A hybrid approach of most recent techniques ( neural networks, fuzzy logic and rule-based) of artificial intelligence is used as the inference engine of the developed system. An object-oriented approach is used in the definition and implementation of the system. An example part is tested and the corresponding process plan is presented to demonstrate and verify the proposed CAPP system. The paper thus suggests a new feature-based intelligent CAPP system for avoiding complex feature recognition and knowledge acquisition problems.Article Citation - WoS: 10Citation - Scopus: 14A Neural Network Model for the Assessment of Partners' Performance in Virtual Enterprises(Springer London Ltd, 2007) Sari, Burak; Amaitik, Saleh; Kilic, S. EnginIn response to increasing international competition, enterprises have been investigating new ways of cooperating with each other to cope with today's unpredictable market behaviour. Advanced developments in information & communication technology (ICT) enabled reliable and fast cooperation to support real-time alliances. In this context, the virtual enterprise (VE) represents an appropriate cooperation alternative and competitive advantage for the enterprises. VE is a temporary network of independent companies or enterprises that can quickly bring together a set of core competencies to take advantage of market opportunity. In this emerging business model of VE, the key to enhancing the quality of decision making in the partner companies' performance evaluation function is to take advantage of the powerful computer-related concepts, tools and technique that have become available in the last few years. This paper attempts to introduce a neural network model, which is able to contribute to the extrapolation of the probable outcomes based on available pattern of events in a virtual enterprise. Quality, delivery and progress were selected as determinant factors effecting the performance assessment. Considering the features of partner performance assessment and neural network models, a back-propagation neural network that includes a two hidden layers was used to evaluate the partner performance.Article Citation - WoS: 5Citation - Scopus: 5A Study on the Performance of Magnetic Material Identification System by Sift-Brisk and Neural Network Methods(Ieee-inst Electrical Electronics Engineers inc, 2015) Ege, Yavuz; Nazlibilek, Sedat; Kakilli, Adnan; Citak, Hakan; Kalender, Osman; Karacor, Deniz; Sengul, GokhanIndustry requires low-cost, low-power consumption, and autonomous remote sensing systems for detecting and identifying magnetic materials. Magnetic anomaly detection is one of the methods that meet these requirements. This paper aims to detect and identify magnetic materials by the use of magnetic anomalies of the Earth's magnetic field created by some buried materials. A new measurement system that can determine the images of the upper surfaces of buried magnetic materials is developed. The system consists of a platform whose position is automatically controlled in x-axis and y-axis and a KMZ51 anisotropic magneto-resistive sensor assembly with 24 sensors mounted on the platform. A new identification system based on scale-invariant feature transform (SIFT)-binary robust invariant scalable keypoints (BRISKs) as keypoint and descriptor, respectively, is developed for identification by matching the similar images of magnetic anomalies. The results are compared by the conventional principal component analysis and neural net algorithms. On the six selected samples and the combinations of these samples, 100% correct classification rates were obtained.Article Citation - WoS: 2Citation - Scopus: 2Discrete Time Neuro Sliding Mode Control With a Task-Specific Output Error(Springer, 2004) Efe, MO; Department of Electrical & Electronics EngineeringThe problem of obtaining the error at the output of a neuro sliding mode controller is analyzed in this paper. The controller operates in discrete time and the method presented describes an error measure that can be used if the task to be achieved is to drive the system under control to a predefined sliding regime. Once the task-specific output error is calculated, the neurocontroller parameters can be tuned so that the task is achieved. The paper postulates the strategy for discrete time representation of uncertain nonlinear systems belonging to a particular class. The performance of the proposed technique has been clarified on a third order nonlinear system, and the parameters of the controller are adjusted by using the error backpropagation algorithm. It is observed that the prescribed behavior can be achieved with a simple network configuration.Conference Object Citation - WoS: 22Citation - Scopus: 21Predicting dominant phytoplankton quantities in a reservoir by using neural networks(Springer, 2003) Gurbuz, H; Kivrak, E; Soyupak, S; Yerli, SVThe Levenberg-Marquardt algorithm was used to train artificial neural networks to predict the abundance of Cyclotella ocellata Pant. and Cyclotella kutzingiana Thwaites using time, depth, temperature, pH, dissolved oxygen, and electrical conductivity as input parameters for the oligo-mesotrophic Kuzgun Dam Reservoir, Turkey. The data were collected in monthly intervals during two ice-free seasons: between April 2000-November 2000 and April 2001-November 2001. To reduce over-fitting of the neural network based models, we employed single hidden layer networks with early stopping of training. Correlation coefficients, of neural network predictions with measurements of abundance of Cyclotella ocellata Pant. and Cyclotella kutzingiana Thwaites were 0.88 and 0.86, respectively.
