DSpace Collection: Volume 15 Number 1
http://hdl.handle.net/10525/8
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Genetic Algorithm for Finding the Key’s Length and Cryptanalysis of the Permutation Cipher
http://hdl.handle.net/10525/302
Title: Genetic Algorithm for Finding the Key’s Length and Cryptanalysis of the Permutation Cipher<br/><br/>Authors: Gorodilov, Aleksey; Morozenko, Vladimir<br/><br/>Abstract: In this article we discuss a possibility to use genetic algorithms in cryptanalysis. We developed and described the genetic algorithm for finding the secret key of a block permutation cipher. In this case key is a permutation of some first natural numbers. Our algorithm finds the exact key’s length and the key with controlled accuracy. Evaluation of conducted experiment’s results shows that the almost automatic cryptanalysis is possible.Neural Networks Diagnostics in Homeopath System
http://hdl.handle.net/10525/303
Title: Neural Networks Diagnostics in Homeopath System<br/><br/>Authors: Katerynych, Larysa; Provotar, Alexander<br/><br/>Abstract: We suppose the neural networks for solution the problem of the diagnostic in Homeopath System and consider the algorithms of the training.A Statistical Convergence Aplication for the Hopfield Networks
http://hdl.handle.net/10525/305
Title: A Statistical Convergence Aplication for the Hopfield Networks<br/><br/>Authors: Gimenez-Martinez, Victor; Sanchez–Torrubia, Gloria; Torres–Blanc, Carmen<br/><br/>Abstract: When Recurrent Neural Networks (RNN) are going to be used as Pattern Recognition systems, the problem to be considered is how to impose prescribed prototype vectorsξ^1,ξ^2,...,ξ^p as fixed points. The synaptic matrix W should be interpreted as a sort of sign correlation matrix of the prototypes, In the classical approach. The weak point in this approach, comes from the fact that it does not have the appropriate tools to deal efficiently with the correlation between the state vectors and the prototype vectors The capacity of the net is very poor because one can only know if one given vector is adequately correlated with the prototypes or not and we are not able to know what its exact correlation degree. The interest of our approach lies precisely in the fact that it provides these tools. In this paper, a geometrical vision of the dynamic of states is explained. A fixed point is viewed as a point in the Euclidean plane R2. The retrieving procedure is analyzed trough statistical frequency distribution of the prototypes. The capacity of the net is improved and the spurious states are reduced. In order to clarify and corroborate the theoretical results, together with the formal theory, an application is presentedData Assimilation Technique For Flood Monitoring and Prediction
http://hdl.handle.net/10525/304
Title: Data Assimilation Technique For Flood Monitoring and Prediction<br/><br/>Authors: Kussul, Natalia; Shelestov, Andrii; Skakun, Serhiy; Kravchenko, Oleksii<br/><br/>Abstract: This paper focuses on the development of methods and cascade of models for flood monitoring and forecasting and its implementation in Grid environment. The processing of satellite data for flood extent mapping is done using neural networks. For flood forecasting we use cascade of models: regional numerical weather prediction (NWP) model, hydrological model and hydraulic model. Implementation of developed methods and models in the Grid infrastructure and related projects are discussed.Intelligent Model of User Behavior in Distributed Systems
http://hdl.handle.net/10525/312
Title: Intelligent Model of User Behavior in Distributed Systems<br/><br/>Authors: Shelestov, Andrii; Skakun, Serhiy; Kussul, Olga<br/><br/>Abstract: We present a complex neural network model of user behavior in distributed systems. The model reflects both dynamical and statistical features of user behavior and consists of three components: on-line and off-line models and change detection module. On-line model reflects dynamical features by predicting user actions on the basis of previous ones. Off-line model is based on the analysis of statistical parameters of user behavior. In both cases neural networks are used to reveal uncharacteristic activity of users. Change detection module is intended for trends analysis in user behavior. The efficiency of complex model is verified on real data of users of Space Research Institute of NASU-NSAU.Solving a Direct Marketing Problem by Three Types of ARTMAP Neural Networks
http://hdl.handle.net/10525/309
Title: Solving a Direct Marketing Problem by Three Types of ARTMAP Neural Networks<br/><br/>Authors: Nachev, Anatoli<br/><br/>Abstract: An important task for a direct mailing company is to detect potential customers in order to avoid unnecessary and unwanted mailing. This paper describes a non-linear method to predict profiles of potential customers using dARTMAP, ARTMAP-IC, and Fuzzy ARTMAP neural networks. The paper discusses advantages of the proposed approaches over similar techniques based on MLP neural networks.Multialgebraic Systems in Information Granulation
http://hdl.handle.net/10525/313
Title: Multialgebraic Systems in Information Granulation<br/><br/>Authors: Kagramanyan, Alexander; Mashtalir, Vladimir; Shlyakhov, Vladislav<br/><br/>Abstract: In different fields a conception of granules is applied both as a group of elements defined by internal properties and as something inseparable whole reflecting external properties. Granular computing may be interpreted in terms of abstraction, generalization, clustering, levels of abstraction, levels of detail, and so on. We have proposed to use multialgebraic systems as a mathematical tool for synthesis and analysis of granules and granule structures. The theorem of necessary and sufficient conditions for multialgebraic systems existence has been proved.Hierarchical Logical Description and Neural Recognition of Complex Patterns
http://hdl.handle.net/10525/301
Title: Hierarchical Logical Description and Neural Recognition of Complex Patterns<br/><br/>Authors: Kosovskaya, Tatiana; Timofeev, Adil<br/><br/>Abstract: Authors suggested earlier hierarchical method for definition of class description at pattern recognition problems solution. In this paper development and use of such hierarchical descriptions for parallel representation of complex patterns on the base of multi-core computers or neural networks is proposed.Networks of Evolutionary Processors: Java Implementation of a Threaded Processor
http://hdl.handle.net/10525/311
Title: Networks of Evolutionary Processors: Java Implementation of a Threaded Processor<br/><br/>Authors: Angel Diaz, Miguel; Fernando de Mingo Lopez, Luis; Gomez Blas, Nuria<br/><br/>Abstract: This paper is focused on a parallel JAVA implementation of a processor defined in a Network of Evolutionary Processors. Processor description is based on JDom, which provides a complete, Java-based solution for accessing, manipulating, and outputting XML data from Java code. Communication among different processor to obtain a fully functional simulation of a Network of Evolutionary Processors will be treated in future. A safe-thread model of processors performs all parallel operations such as rules and filters. A non-deterministic behavior of processors is achieved with a thread for each rule and for each filter (input and output). Different results of a processor evolution are shown.Networks of Evolutionary Processors (NEP) as Decision Support Systems
http://hdl.handle.net/10525/307
Title: Networks of Evolutionary Processors (NEP) as Decision Support Systems<br/><br/>Authors: Gomez Blas, Nuria; Angel Diaz, Miguel; Castellanos, Juan; Serradilla, Francisco<br/><br/>Abstract: This paper presents the application of Networks of Evolutionary Processors to Decision Support Systems, precisely Knowledge-Driven DSS. Symbolic information and rule-based behavior in Networks of Evolutionary Processors turn out to be a great tool to obtain decisions based on objects present in the network. The non-deterministic and massive parallel way of operation results in NP-problem solving in linear time. A working NEP example is shown.Forming of Learning Set for Neural Networks in Problems of Losless Data Compression
http://hdl.handle.net/10525/314
Title: Forming of Learning Set for Neural Networks in Problems of Losless Data Compression<br/><br/>Authors: Ivaskiv, Yuriy; Levchenko, Victor<br/><br/>Abstract: questions of forming of learning sets for artificial neural networks in problems of lossless data compression are considered. Methods of construction and use of learning sets are studied. The way of forming of learning set during training an artificial neural network on the data stream is offered.The Fuzzy-Neuro Classifier for Decision Support
http://hdl.handle.net/10525/308
Title: The Fuzzy-Neuro Classifier for Decision Support<br/><br/>Authors: Setlak, Galina<br/><br/>Abstract: This paper aims at development of procedures and algorithms for application of artificial intelligence tools to acquire process and analyze various types of knowledge. The proposed environment integrates techniques of knowledge and decision process modeling such as neural networks and fuzzy logic-based reasoning methods. The problem of an identification of complex processes with the use of neuro-fuzzy systems is solved. The proposed classifier has been successfully applied for building one decision support systems for solving managerial problem.Almost Separable Data Aggregation by Layers of Formal Neurons
http://hdl.handle.net/10525/306
Title: Almost Separable Data Aggregation by Layers of Formal Neurons<br/><br/>Authors: Bobrowski, Leon<br/><br/>Abstract: Information extraction or knowledge discovery from large data sets should be linked to data aggregation process. Data aggregation process can result in a new data representation with decreased number of objects of a given set. A deterministic approach to separable data aggregation means a lesser number of objects without mixing of objects from different categories. A statistical approach is less restrictive and allows for almost separable data aggregation with a low level of mixing of objects from different categories. Layers of formal neurons can be designed for the purpose of data aggregation both in the case of deterministic and statistical approach. The proposed designing method is based on minimization of the of the convex and piecewise linear (CPL) criterion functions.Selfstructurized Systems
http://hdl.handle.net/10525/310
Title: Selfstructurized Systems<br/><br/>Authors: Gladun, Victor; Velychko, Vitalii; Ivaskiv, Yurii<br/><br/>Abstract: The problems of constructing the selfsrtucturized systems of memory of intelligence information processing tools, allowing formation of associative links in the memory, hierarchical organization and classification, generating concepts in the process of the information input, are discussed. The principles and methods for realization of selfstructurized systems on basis of hierarchic network structures of some special class – growing pyramidal network are studied. The algorithms for building, learning and recognition on basis of such type network structures are proposed. The examples of practical application are demonstrated.