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An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: a case study Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2021-01-04 Alireza Goli; Hasan Khademi-Zare; Reza Tavakkoli-Moghaddam; Ahmad Sadeghieh; Mazyar Sasanian; Ramina Malekalipour Kordestanizadeh
ABSTRACT This research specifically addresses the prediction of dairy product demand (DPD). Since dairy products have a short consumption period, it is important to have accurate information about their future demand. The main contribution of this research is to provide an integrated framework based on statistical tests, time-series neural networks, and improved MLP, ANFIS, and SVR with novel meta-heuristic
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A novel Chebyshev neural network approach for solving singular arbitrary order Lane-Emden equation arising in astrophysics Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2020-11-05 Susmita Mall; S. Chakraverty
ABSTRACT The motivation of this investigation is to develop a single-layer Chebyshev Neural Network (ChNN) model to handle singular fractional (arbitrary)-order Lane-Emden type equations. These equations are well-known application problems of astrophysics and quantum mechanics. Fractional Lane-Emden equations are singular so it is very difficult to solve analytically. Thus, an efficient method is required
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Data classification based on fractional order gradient descent with momentum for RBF neural network Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2020-12-06 Han Xue; Zheping Shao; Hongbo Sun
ABSTRACT The weight-updating methods have played an important role in improving the performance of neural networks. To ameliorate the oscillating phenomenon in training radial basis function (RBF) neural network, a fractional order gradient descent with momentum method for updating the weights of RBF neural network (FOGDM-RBF) is proposed for data classification. Its convergence is proved. In order
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The formation and use of hierarchical cognitive maps in the brain: A neural network model. Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2020-08-03 Henry O C Jordan,Daniel M Navarro,Simon M Stringer
ABSTRACT Many researchers have tried to model how environmental knowledge is learned by the brain and used in the form of cognitive maps. However, previous work was limited in various important ways: there was little consensus on how these cognitive maps were formed and represented, the planning mechanism was inherently limited to performing relatively simple tasks, and there was little consideration
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Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey. Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2020-05-13 Emre Yakut,Seval Süzülmüş
ABSTRACT The accurate modelling and prediction of air temperature values is an exceptionally important meteorological variable that affects in many areas. The present study is aimed at developing models for the prediction of monthly mean air temperature values in Turkey using ANN, ANFIS and SVMr methods. In developing the models, the monthly data derived from eight stations of the TSMS for the 1963–2015
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A fractional power series neural network for solving a class of fractional optimal control problems with equality and inequality constraints. Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2019-12-17 Safiye Ghasemi,Alireza Nazemi
This paper solved fractional order optimal control problems, in which the dynamic control system involves integer and fractional order derivatives with equality and inequality constraints. According to the Pontryagin minimum principle (PMP) for fractional optimal control problem (FOCP) with fractional derivative in the Caputo sense and by constructing a suitable error function, an unconstrained minimization
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Fractional infinite-horizon optimal control problems with a feed forward neural network scheme Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2019-11-14 Mina Yavari; Alireza Nazemi
This paper presents a method based on neural networks to solve fractional infinite-horizon optimal control problems s(FIHOCP)s, where the dynamic control system depends on Caputo fractional derivatives. First, with the help of an approximation, the Caputo derivative is replaced to integer-order derivative. Using a suitable change of variable, the IHOCP is transformed into a finite-horizon one. According
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Alternative continuous- and discrete-time neural networks for image restoration Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2019-10-30 Yawei Li; Xingbao Gao
This paper presents alternative continuous- and discrete-time neural networks for image restoration in real time by introducing new vectors and transforming its optimization conditions into a system of double projection equations. The proposed neural networks are shown to be stable in the sense of Lyapunov and convergent for any starting point. Compared with the existing neural networks for image restoration
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Goal-directed autonomous navigation of mobile robot based on the principle of neuromodulation Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2019-09-30 Dongshu Wang; Wenjie Si; Yong Luo; Heshan Wang; Tianlei Ma
Autonomous navigation in dynamic environment is aprerequisite of the mobile robot to perform tasks, and numerous approaches have been presented, including the supervised learning. Using supervised learning in robot navigation might meet problems, such as inconsistent and noisy data, and high error in training data. Inspired by the advantages of the reinforcement learning, such as no need for desired
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Computational architecture of a visual model for biological motions segregation Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2019-08-21 L. I. Abdul-Kreem
This paper outlines a neural model inspired by the dorsal stream of the visual system for motion recognition. Two areas are considered: the primary visual area (V1) and the middle temporal area (MT). In model area, V1 neurons are organized to detect eight local motion directions. MT is modelled using classical receptive field (CRF), where the cells respond to wide-field motion. The biological motion
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Automatic and heuristic complete design for ANFIS classifier Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2019-08-26 Amir Soltany Mahboob; Seyed Hamid Zahiri
There is a variety of fuzzy classifiers, one of which is Adaptive Neuro-Fuzzy Inference system (ANFIS) classifier. One of the main challenges in designing such data classifiers is selection of effective and appropriate type and location of membership functions and its training method to reduce the classification error. In this paper, a new technique (based on intelligent methods) is presented and implemented
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Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest Netw. Comput. Neural. Syst. (IF 0.5) Pub Date : 2019-06-26 Mayadeh Kouti; Karim Ansari-Asl; Ehsan Namjoo
We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic