New results for global stability of a class of neutral-type neural systems with time delays

This paper studies the global convergence properties of a class of neutral-type neural networks with discrete time delays. This class of neutral systems includes Cohen-Grossberg neural networks, Hopfield neural networks and cellular neural networks. Based on the Lyapunov stability theorems, some delay independent sufficient conditions for the global asymptotic stability of the equilibrium point for this class of neutral-type systems are derived. It is shown that the results presented in this paper for neutral-type delayed neural networks are the generalization of a recently reported stability result. A numerical example is also given to demonstrate the applicability of our proposed stability criteria. (C) 2009 Elsevier Inc. All rights reserved

Global robust stability analysis of uncertain neural networks with time varying delays

This paper deals with the global robust stability analysis of dynamical neural networks with time varying delays. By combining Lyapunov stability theorems and Homeomorphic mapping theorem, we obtain some original sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point with respect to Lipschitz activation functions and under parameter uncertainties of the neural system. We also prove that the obtained robust stability conditions generalize some of the previously published corresponding literature results. The conditions we present can be easily verified as the conditions that are expressed in terms of the network parameters. Some comparative numerical examples are presented to demonstrate the advantages of our conditions over the previously published robust stability results. (C) 2015 Elsevier B.V. All rights reserved

Global convergence analysis of delayed bidirectional associative memory neural networks

This paper studies the stability properties of a more general class of bidirectional associative memory (BAM) neural networks with constant time delays. Without assuming the symmetry of the interconnection matrices, and monotonicity and differentiability of the activation functions, we derive a new sufficient condition for the global asymptotic stability of the equilibrium point for bidirectional associative memory neural networks. The obtained results are independently of the delay parameters and can be easily verified. The results are also compared with the previous results derived in the literature

Number Recognition of Sudoku Grid Image with Artificial Neural Networks

In this study it is aimed to capture a SUDOKU grid image, to process this image, to recognize the numbers in the grid image with Artificial Neural Networks and finally to constitute a 9 x 9 number array with these numbers. The reason of choosing SUDOKU game as the input material is the thought of SUDOKU game as a prototype of real world fitting problems. After this number recognition is completed successfully, a robot software who finds the right solution of a SUDOKU game automatically will be developed. The next aim of this robot software is solving real world fitting problems

SOLVING SUDOKU PUZZLE with NUMBERS RECOGNIZED by USING ARTIFICIAL NEURAL NETWORKS

This paper proposed a method to solve 9x9 SUDOKU puzzles automatically. To this end, a captured puzzle image is used, the numbers in this image are recognized by using Artificial Neural Networks (ANN) and a 9x9 number array with these numbers is constituted, respectively. Then, the proposed method is applied to the prepared numerical array for solving the puzzle. The validity of the proposed method is demonstrated with results from an example 9 x 9 SUDOKU puzzle image

Comparative study of modeling the stability improvement of sunflower oil with olive leaf extract

Commercially available sunflower oil was enriched in polyphenols by adding olive leaf extract. After extracting the dried and ground olive leaves with the assistance of homogenizer, total phenolic content (TPC) and oleuropein concentration of the extract were determined. The dried extract was partially dissolved into the sunflower oil to increase the quality and shelf-life of the oil enriched by the substances in the plants by means of solid-liquid extraction method. A face central composite design (FCCD) through response surface methodology (RSM) was used to investigate the effects of enrichment conditions (extract content, time and mixing speed) on the responses, TPC and oleuropein concentration of the enriched sunflower oil as well as to design of experiments, to model and to optimize the process. The enriched sunflower oil obtained at optimum conditions was evaluated in terms of its TPC, oleuropein, total carotenoid content (TCC), antioxidant activity (AA), peroxide value (PV) and induction time (IT), depending on those of the crude oil. Furthermore, artificial neural networks (ANN) were also employed to compare the predicted results of RSM

Forecasting Air Travel Demand for Selected Destinations Using Machine Learning Methods

Over the past decades, air transportation has expanded and big data for transportation era has emerged. Accurate travel demand information is an important issue for the transportation systems, especially for airline industry. So, “optimal seat capacity problem between origin and destination pairs” which is related to the load factor must be solved. In this study, a method for determining optimal seat capacity that can supply the highest load factor for the flight operation between any two countries has been introduced. The machine learning methods of Artificial Neural Network (ANN), Linear Regression (LR), Gradient Boosting (GB), and Random Forest (RF) have been applied and a software has been developed to solve the problem. The data set generated from The World Bank Database, which consists of thousands of features for all countries, has been used and a case study has been done for the period of 2014-2019 with Turkish Airlines. To the best of our knowledge, this is the first time that 1983 features have been used to forecast air travel demand in the literature within a model that covers all countries while previous studies cover only a few countries using far fewer features. Another valuable point of this study is the usage of the last regular data about the air transportation before COVID-19 pandemic. In other words, since many airline companies have experienced a decline in the air travel operation in 2020 due to COVID-19 pandemic, this study covers the most recent period (2014-2019) when flight operation performed on a regular basis. As a result, it has been observed that the developed model has forecasted the passenger load factor by an average error rate of 6.741% with GB, 6.763% with RF, 8.161% with ANN, and 9.619 % with LR

Modeling The Toxicity of Textile Industry Wastewater Using Artificial Neural Networks

Toxicity tests are required to detect the possible effects of pollutants on organisms. This study investigates the effect of Chemical Oxygen Demand (COD), suspended solid (SS) and pH parameters on toxicity of textile industry wastewaters except for the color parameter, effect of which is well known. Fish bioassay taking place in legal regulation of Turkey was used as toxicity test. At the end of the toxicity test, various values of the parameters were predicted through Artificial Neural Networks (ANN). In addition, Artificial Neural Networks were used to calculate the effect of each parameter on toxicity (%). Accordingly, COD is the parameter which mostly affects toxicity following color parameter and SS is the parameter which has the minimum effect. It is found that results deviate at the rate of 15.41% when values of COD parameter are excluded from the model input data and the error rate becomes 5.07% when SS parameter is excluded. In this study, the effect of each input of each parameter, which is an open ecosystem, based on selected parameters is successfully predicted through Artificial Neural Networks which is a heuristic method

Some generalized global stability criteria for delayed Cohen-Grossberg neural networks of neutral-type

This paper carries out a theoretical investigation into the stability problem for the class of neutral-type Cohen-Grossberg neural networks with discrete time delays in states and discrete neutral delays in time derivative of states. By employing a more general type of suitable Lyapunov functional, a set of new generalized sufficient criteria are derived for the global asymptotic stability of delayed neural networks of neutral-type. The proposed stability criteria are independently of the values of the time delays and neutral delays, and they completely rely on some algebraic mathematical relationships involving the values of the elements of the interconnection matrices and the other network parameters. Therefore, it is easy to verify the validity of the obtained results by simply using some algebraic equations representing the stability conditions. A detailed comparison between our proposed results and recently reported corresponding stability results is made, proving that the results given in this paper generalize previously published stability results. A constructive numerical example is also given to demonstrate the applicability of the results of the paper. (C) 2019 Elsevier Ltd. All rights reserved