Instantaneous modelling and reverse engineering of data-consistent prime models in seconds!

A theoretical framework that supports automated construction of dynamic prime models purely from experimental time series data has been invented and developed, which can automatically generate (construct) data-driven models of any time series data in seconds. This has resulted in the formulation and formalisation of new reverse engineering and dynamic methods for automated systems modelling of complex systems, including complex biological, financial, control, and artificial neural network systems. The systems/model theory behind the invention has been formalised as a new, effective and robust system identification strategy complementary to process-based modelling. The proposed dynamic modelling and network inference solutions often involve tackling extremely difficult parameter estimation challenges, inferring unknown underlying network structures, and unsupervised formulation and construction of smart and intelligent ODE models of complex systems. In underdetermined conditions, i.e., cases of dealing with how best to instantaneously and rapidly construct data-consistent prime models of unknown (or well-studied) complex system from small-sized time series data, inference of unknown underlying network of interaction is more challenging. This article reports a robust step-by-step mathematical and computational analysis of the entire prime model construction process that determines a model from data in less than a minute

Model of cybersecurity means financing with the procedure of additional data obtaining by the protection side

The article describes the model of cybersecurity means financing strategies of the information object with incomplete information about the financial resources of the attacking side. The proposed model is the core of the module of the developed decision support system in the problems of choosing rational investing variants for information protection and cybersecurity of various information objects. The model allows to find financial solutions using the tools of the theory of multistep games with several terminal surfaces. The authors proposed an approach that allows information security management to make a preliminary assessment of strategies for financing the effective cybersecurity systems. The model is distinguished by the assumption that the protection side does not have complete information, both about the financing strategies of the attacking side, and about its financial resources state aimed at overcoming cybersecurity lines of the information object. At the same time, the protection side has the opportunity to obtain additional information by the part of its financial resources. This makes it possible for the protection side to obtain a positive result for itself in the case when it can not be received without this procedure. The solution was found using a mathematical apparatus of a nonlinear multistep quality game with several terminal surfaces with alternate moves. In order to verify the adequacy of the model there was implemented a multivariate computational experiment. The results of this experiment are described in the article. © 2005 - ongoing JATIT & LL

What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed

Towards a Comprehensible and Accurate Credit Management Model: Application of four Computational Intelligence Methodologies

The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feedforward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimize significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in bankin

Modelling of Metallurgical Processes Using Chaos Theory and Hybrid Computational Intelligence

The main objective of the present work is to develop a framework for modelling and controlling of a real world multi-input and multi-output (MIMO) continuously drifting metallurgical process, which is shown to be a complex system. A small change in the properties of the charge composition may lead to entirely different outcome of the process. The newly emerging paradigm of soft-computing or Hybrid Computational Intelligence Systems approach which is based on neural networks, fuzzy sets, genetic algorithms and chaos theory has been applied to tackle this problem In this framework first a feed-forward neuro-model has been developed based on the data collected from a working Submerged Arc Furnace (SAF). Then the process is analysed for the existence of the chaos with the chaos theory (calculating indices like embedding dimension, Lyapunov exponent etc). After that an effort is made to evolve a fuzzy logic controller for the dynamical process using combination of genetic algorithms and the neural networks based forward model to predict the system’s behaviour or conditions in advance and to further suggest modifications to be made to achieve the desired results