Intuitionistic Fuzzy Hopfield Neural Network and Its Stability

Abstract

Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment. As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks. Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions. In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection. For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used. As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms. A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).

Interval Neutrosophic Sets and Their Application in Multicriteria Decision Making Problems

“…Later on, Bustince pointed out that vague sets and Atanassov's IFSs are mathematically equivalent objects [ 9 ]. As for the present, IFSs have been widely applied in solving multicriteria decision making problems [ 10 – 14 ], neural networks [ 15 , 16 ], medical diagnosis [ 17 ], color region extraction [ 18 , 19 ], market prediction [ 20 ], and so forth.…”

Cross-Entropy and Prioritized Aggregation Operator with Simplified Neutrosophic Sets and Their Application in Multi-Criteria Decision-Making Problems

“…Because of the advantages that an IFS considers the membership-degree, non-membership degree and hesitation degree simultaneously, it is more flexible and useful to describe the uncertain information than a traditional FS. Thus, many methods based on IFSs have been put forward and widely applied to solve MCDM problems [6][7][8][9][10][11][12], medical diagnosis [13,14], pattern recognition [15,16], stock market prediction [17,18], and marketing strategy selection [19]. However, in some real situations, the membership degree, non-membership degree and hesitation degree may be difficultly given by specific numbers; hence, interval-valued intuitionistic fuzzy sets (IVIFSs) [20] are developed and applied to solve such problems [21][22][23][24][25].…”

Anti-Spurious-State Neural Network Using Nonlinear Outer Product and Dynamic Synapses

“…In most of traditional neural network models [7,15,25,31,[38][39][40], the synaptic weights are assumed to be 'static', or changing their weight values only on a slow time pace. Accordingly, once the weights of the synapses are for example determined by a covariance rule, they are fixed during the network evolution process.…”