Pattern Analysis and Intelligent Systems
OLATUNJI HEZEKIAH ADIGUN; OLUSOLA JOEL OYEDELE
Volume 5, Issue 1 , February 2019, , Pages 11-18
Abstract
This paper employs Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict water level that leads to flood in coastal areas. ANFIS combines the verbal power of fuzzy logic and numerical power of neural network for its action. Meteorological and astronomical data of Santa Monica, a coastal area in California, ...
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This paper employs Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict water level that leads to flood in coastal areas. ANFIS combines the verbal power of fuzzy logic and numerical power of neural network for its action. Meteorological and astronomical data of Santa Monica, a coastal area in California, U. S. A., were obtained. A portion of the data was used to train the ANFIS network, while other portions were used to check and test the generalization ability of the ANFIS model. Water level predictions were made for 24 hours, 48 hours and 72 hours, in which training, checking and testing of the model were performed for each of the prediction periods. The model results from the training, checking and testing data groups show that 48 hours prediction has the least Root Mean Square Error (RMSE) of 0.05426, 0.06298 and 0.05355 for training, checking and testing data groups respectively, showing that the prediction is most accurate for 48 hours.
Computer Networks and Distributed Systems
mohammadreza hosseinzadehmoghadam; seyed javad mirabedini; toraj banirostam
Volume 3, Issue 4 , November 2017, , Pages 213-222
Abstract
Abstract— In order to provide complete security in a computer system and to prevent intrusion, intrusion detection systems (IDS) are required to detect if an attacker crosses the firewall, antivirus, and other security devices. Data and options to deal with it. In this paper, we are trying to provide ...
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Abstract— In order to provide complete security in a computer system and to prevent intrusion, intrusion detection systems (IDS) are required to detect if an attacker crosses the firewall, antivirus, and other security devices. Data and options to deal with it. In this paper, we are trying to provide a model for combining types of attacks on public data using combined methods of genetic algorithm and neural network. The goal is to make the designed model act as a measure of system attack and combine optimization algorithms to create the ultimate accuracy and reliability for the proposed model and reduce the error rate. To do this, we used a feedback neural network, and by examining the worker, it can be argued that this research with the new approach reduces errors in the classification.with the rapid development of communication and information technology and its applications, especially in computer networks, there is a new competition in information security and network security.
Maryam Ashtari Mahini; Mohammad Teshnehlab; Mojtaba Ahmadieh khanehsar
Volume 1, Issue 3 , August 2015, , Pages 1-8
Abstract
Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them. TheHammerstein-Wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two ...
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Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them. TheHammerstein-Wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsystem identification by Hammerstein-Wiener neural networkis finding model order, state matrices and system matrices. Wepropose a robust approach for identifying the nonlinear systemby neural network and subspace algorithms. The subspacealgorithms are mathematically well-established and noniterativeidentification process. The use of subspace algorithmmakes it possible to directly obtain the state space model.Moreover the order of state space model is achieved usingsubspace algorithm. Consequently, by applying the proposedalgorithm, the mean squared error decreases to 0.01 which isless than the results obtained using most approaches in theliterature.