Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets

The development of new models that would enhance predictability for time series with dynamic time-varying, nonlinear features is a major challenge for speculators. Boundedly rational investors called “chartists” use advanced heuristics and rules-of-thumb to make profit by trading, or even hedge against potential market risks. This paper introduces a hybrid neurofuzzy system for decision-making and trading under uncertainty. The efficiency of a technical trading strategy based on the neurofuzzy model is investigated, in order to predict the direction of the market for 10 of the most prominent stock indices of U.S.A, Europe and Southeast Asia. It is demonstrated via an extensive empirical analysis that the neurofuzzy model allows technical analysts to earn significantly higher returns by providing valid information for a potential turning point on the next trading day. The total profit of the proposed neurofuzzy model, including transaction costs, is consistently superior to a recurrent neural network and a Buy & Hold strategy for all indices, particularly for the highly speculative, emerging Southeast Asian markets. Optimal prediction is based on the dynamic update and adaptive calibration of the heuristic fuzzy learning rules, which reflect the psychological and behavioral patterns of the traders.     

On Detecting Solutions of Polynomial Nonlinear Difference Equations

In this paper a method for discovering solutions of nonlinear polynomial difference equations is presented. It is based on the concepts of i -operator and star-product. These notions create a proper algebraic background by means of which we can find linear equations "included" into the original nonlinear one and to seek for solutions among them. A corresponding algorithm and some examples are also provided.     

Necessary and sufficient conditions for robust global asymptotic stabilization of discrete-time systems

We give Lyapunov-like conditions for non-uniform in time output stabilization of discrete-time systems. Particularly, it is proved that for a discrete-time control system there exists a (continuous) output stabilizing feedback if and only if there exists a (strong) output control Lyapunov function (OCLF). Moreover, strategies for the construction of continuous robust feedback stabilizers are presented.     

Redesigning product lines in a period of economic crisis: a hybrid simulated annealing algorithm with crossover

Bandit products have captured significant market shares in China and have started to expand throughout the world. A striking feature of supply chains for bandit products is decentralization, where the upstream firm determines the product quality and the downstream firms compete on prices. We study the competition between a centralized mainstream firm and a decentralized bandit supply chain. We demonstrate that the structural difference between the mainstream firm and the bandit supply chain reduces competition intensity and the quality difference between their products. Surprisingly, the inherent inefficiency in a bandit supply chain, combined with the force of competition, actually leads to both higher product quality and higher price. Furthermore, due to the free-riding effect, the bandit supply chain may even offer higher quality products than the mainstream firm. The mainstream firm’s profit as a function of the free-riding effect is U-shaped, so that free-riding by the bandit supply chain may eventually benefit the mainstream firm. Finally, decentralization benefits the bandit supply chain when the competition is on product features.     

Synchronization of the Glycolysis Reaction-Diffusion Model via Linear Control Law

The Selkov system, which is typically employed to model glycolysis phenomena, unveils some rich dynamics and some other complex formations in biochemical reactions. In the present work, the synchronization problem of the glycolysis reaction-diffusion model is handled and examined. In addition, a novel convenient control law is designed in a linear form and, on the other hand, the stability of the associated error system is demonstrated through utilizing a suitable Lyapunov function. To illustrate the applicability of the proposed schemes, several numerical simulations are performed in one- and two-spatial dimensions.     

A LOI-RCCE methodology for reducing chemical kinetics, with application to laminar premixed flames

A framework for automated development of reduced mechanisms is presented by combining the methodologies of level of importance (LOI) and rate-controlled constrained equilibrium (RCCE). It is shown that these methods are complimentary, as they deal with different aspects of the overall reduction problem: LOI is a method of determining the species governed by fast/slow scales, while RCCE is a physical model that yields ODEs that describe the reduced model for a given selection. The potential for the synergy of the two methods is demonstrated by reducing a comprehensive mechanism for propane combustion (117 species, 665 reactions) and applying the reduced mechanism to the problem of 1-D laminar flame propagation. Results show that RCCE yields substantial CPU gainings, while predicting the major species as well as important minor species (such as radicals and C₂H₂) with sufficient accuracy.     

Simultaneous EEG and fMRI in the macaque monkey at 4.7 Tesla

Simultaneous electroencephalography (EEG)/functional magnetic resonance imaging (fMRI) acquisition can identify the brain networks involved in generating specific EEG patterns. Yet, the combination of these methodologies is hampered by strong artifacts that arise due to electromagnetic interference during magnetic resonance (MR) image acquisition. Here, we report corrections of the gradient-induced artifact in phantom measurements and in experiments with an awake behaving macaque monkey during fMRI acquisition at a magnetic field strength of 4.7 T. Ninety-one percent of the amplitude of a 10 microV, 10 Hz phantom signal could successfully be recovered without phase distortions. Using this method, we were able to extract the monkey EEG from scalp recordings obtained during MR image acquisition. Visual evoked potentials could also be reliably identified. In conclusion, simultaneous EEG/fMRI acquisition is feasible in the macaque monkey preparation at 4.7 T and holds promise for investigating the neural processes that give rise to particular EEG patterns.     

Differential Diffusion Modelling in LES with RCCE-Reduced Chemistry

The present work shows results obtained from the incorporation of a soot model into a combined Large Eddy Simulation and Conditional Moment Closure approach to modelling turbulent non-premixed flames. Soot formation is determined via the solution of two transport equations for soot mass fraction and particle number density, where acetylene is employed as the incipient species responsible for soot nucleation. The concentrations of the gaseous species are calculated using a Rate-Controlled Constrain Equilibrium approach to reduce the number of species to solve from a detailed gas-phase kinetic scheme involving 63 species. The study focuses on the influence of differential diffusion of soot particles on soot volume fraction predictions. The results of calculations are compared with experimental data for atmospheric methane flames, Overall, the study demonstrates that the model, when used in conjunction with a representation of differential diffusion effects, is capable of predicting soot formation at a fundamental level in the turbulent non- premixed flames considered.