IEEE Computational Intelligence for Financial Engineering and Economics (IEEE CIFEr 2022)

Modelling Financial Time Series with Switching State Space Models - Computational Intelligence for Financial Engineering, 1999. (CIFEr). Proceedings of the IEEE/IAFE

The deficiencies of stationary models applied to financial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state ...

What Will 2022 Look Like? The IEEE CS 2022 Report

August 2010 | Ieee Computational Intelligence Magazine 11

The manual design of control systems for robotic devices can be challenging. Methods for the automatic synthesis of control systems, such as the evolution of artificial neural networks, are thus widely used in the robotics community. However, in many robotic tasks where multiple interdependent control problems have to be solved simultaneously, the performance of conventional neuroevolution tech...

August 2010 | Ieee Computational Intelligence Magazine 33

In this paper, we show how a simulated humanoid robot controlled by an artificial neural network can acquire the ability to manipulate spherical objects located over a table by reaching, grasping, and lifting them. The robot controller is developed through an adaptive process in which the free parameters encode the control rules that regulate the fine-grained interaction between the agent and t...

February 2008 | Ieee Computational Intelligence Magazine 31

rtificial neural networks (ANNs) and evolutionary algorithms (EAs) are both abstractions of natural processes. Since early 1990s, they have been combined into a general and unified computational model of adaptive systems, i.e., Evolutionary ANNs (EANNs) [52], [54], to utilize the learning power of ANNs and adaptive capabilities of EAs. EANNs refer to a special class of ANNs in which evolution i...