Fractional order dynamical systems and its applications

This article illustrates several applications of fractional calculus (FC) in science and engineering. It has been recognized the advantageous use of this mathematical tool in the modeling and control of many dynamical systems. In this perspective, this paper investigates the use of FC in the following fields: Controller tuning; Electrical systems; Traffic systems; Digital circuit synthesis; Evolutionary computing; Redundant robots; Legged robots; Robotic manipulators; Nonlinear friction; Financial modeling.N/

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad

Evolutionary multi-stage financial scenario tree generation

Multi-stage financial decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future returns of the selected assets or asset categories. Various approaches towards an optimal generation of discrete-time, discrete-state approximations (represented as scenario trees) have been suggested in the literature. In this paper, a new evolutionary algorithm to create scenario trees for multi-stage financial optimization models will be presented. Numerical results and implementation details conclude the paper

Soft computing in investment appraisal

Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. Such approach accounts for what occurs most of the time in the market, but does not reflect the reality, as major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK companies traded on the London Stock Exchange and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we suggest a specific evolutionary algorithm to train a fuzzy neural net - the bidirectional incremental evolution will automatically identify the complexity of the problem and correspondingly adapt the parameters of the fuzzy network

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

A revised perspective on the evaluation of IT/IS investments using an evolutionary approach

On-going research into the evaluation of Information Technology (IT) / Information Systems (IS) projects has shown that aerospace and supply chain industries are needing to address the issue of effective project investment in order to gain technological and competitive advantage. The evaluative nature of the justification process requires a mapping of interrelated quantities to be optimised. Earlier work by the authors (Irani and Sharif 1997) has presented a theoretical functional model that describes these relationships in turn. By applying a fuzzy mapping to these variables, the optimisation of intangible relationships in the form of a Genetic Algorithm (GA) is proposed as a method for investment justification. This paper revises and reviews these key concepts and provides a recapitulation of this optimisation problem in terms of long-term strategy options and cost implications. Glossary of terms : DC = Direct Costs, FA = Financial Appraisal, FR = Financial Risks, FUR = Functional Risks, HC = Human Costs, IC = Indirect Costs, IR = Infrastructural Risks, OB = Operational Benefits, OC = Organisational Costs, PB = Project Benefits, PC = Project Costs, RF = Risk Factor, SB = Strategic Benefits, SM = Strategic medium-term benefit, SR = Systemic Risks, TB = Tangible Benefits, TC = Tangible Costs, TL = project lead time, TR = Technological Risks, V= Project Value

Electrical power grid network optimisation by evolutionary computing

A major factor in the consideration of an electrical power network of the scale of a national grid is the calculation of power flow and in particular, optimal power flow. This paper considers such a network, in which distributed generation is used, and examines how the network can be optimized, in terms of transmission line capacity, in order to obtain optimal or at least high-performing configurations, using multi-objective optimisation by evolutionary computing methods