Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

Towards cognitive economy

Abstract: This publication discusses new development directions of cognitive economics charted based on cognitive processes of financial data analysis. Semantic analysis tasks will be dedicated to cognitive economics systems. Such systems, which semantically analyse the economic situation of enterprises/organisations/business structures, execute their tasks using semantic information available to them (contained in data sets). The foundation for the operation of economic cognitive systems consists in both the semantic analysis of the situation of an enterprise described by various economic/financial ratios and the assessment of the future situation of this enterprise. Hence cognitive economics is geared towards the semantic analysis of the economic/financial situation of enterprises carried out by means of an in-depth description, an analysis, a reasoning and a projection of the future condition of enterprises. The subject of cognitive economics discussed for the purpose of indicating and elaborating on the directions of this discipline and of semantic analysis of economic/financial data goes hand in hand with the currently growing trend of soft computing. Methods of semantic analysis, just like e.g. neural networks, were developed by researchers inspired by the operation of the human mind. The cognitive, decision-making, reasoning, understanding and prediction processes running in this mind have become the basis for attempts to create information systems analysing various data. This type of analysis, based on extracting semantic aspects and information from the analysed data sets has been aimed at using computational methods to help solve various problems. Since such solutions are dedicated to bioinformatics, cognitive informatics and artificial intelligence, it appeared likely that they could also be used in economics. It is in this regard that the development of semantic data analysis methods (dedicated to economic/financial problems) puts cognitive economics in the same group with all other scientific disciplines making use of soft computing techniques.

Computing approximately, and efficiently

Abstract: Recent years have witnessed significant interest in the area of approximate computing. Much of this interest stems from the quest for new sources of computing efficiency in the face of diminishing benefits from technology scaling. We argue that trends in computing workloads will greatly increase the opportunities for approximate computing, describe the vision and key principles that have guided our work in this area, and outline a range of approximate computing techniques that we have developed at all layers of the computing stack, spanning circuits, architecture, and software.

Soft Computing Methodologies in Bioinformatics

Abstract: Bioinformatics is a promising and innovative research field in 21st century. Despite of a high number of techniques specifically dedicated to bioinformatics problems as well as many successful applications, we are in the beginning of a process to massively integrate the aspects and experiences in the different core subjects such as biology, medicine, computer science, engineering, chemistry, physics, and mathematics. Recently the use of soft computing tools for solving bioinformatics problems have been gaining the attention of researchers because of their ability to handle imprecision, uncertainty in large and complex search spaces. The paper will focus on soft computing paradigm in bioinformatics with particular emphasis on integrative research.

Service Restoration With Prioritization of Customers and Switches and Determination of Switching Sequence

Abstract: Distribution system (DS) service restoration (SR) in contingency situations is one of the most complex and challenging problems in DS operation. It is usually formulated as a multi-objective and multi-constraint optimization problem that must be quickly solved. Several methods have been proposed for its solution, however, most of them still have limitations. Some demand long running time when applied to large-scale DSs modeled with no simplification, whereas others disregard some important aspects of the SR problem. This paper proposes a methodology based on multi-objective evolutionary algorithms for the SR problem and overcoming of such limitations. In contrast to methods reported in the literature, the methodology: 1) deals with large-scale DSs with relatively soft computing time and requires no network topology simplification; 2) prioritizes the operation of remotely controlled switches; 3) prioritizes supply to three levels of priority customers; and 4) provides switching sequences. A mathematical formulation of the problem is also proposed. Several tests were conducted for the evaluation of the methodology and single and multiple fault cases in large-scale DSs (from 631 to 5158 switches) were considered.