14169547
Non-linear modelling and chaotic neural networks
Proposes a simple methodology to construct an iterative neural network which mimics a given chaotic time series. The methodology uses the Gamma test to identify a suitable (possibly irregular) embedding of the chaotic time series from which a one step predictive model may be constructed. This model is then iterated to produce a close approximation to the original chaotic dynamics. Having constructed such networks we show how the chaotic dynamics may be stabilised using time-delayed feedback, which is a plausible method for stabilisation in biological neural systems. Using delayed feedback control, which is activated in the presence of a stimulus, such networks can behave as an associative memory, in which the act of recognition corresponds to stabilisation onto an unstable periodic orbit. We briefly illustrate how two identical dynamically independent copies of such a chaotic iterative network may be synchronised using the delayed feedback method. Although less biologically plausible, these techniques may have interesting applications in secure communications.
9858437
Heuristic Confidence Intervals for the Gamma Test
The Gamma test is a fast non-parametric algorithm for estimating the noise variance in an input/output dataset modulo the best smooth model. In this short note we communicate a heuristic method for computing confidence intervals for the noise estimate returned by a Gamma test analysis.
11348191
A note on the Gamma test
This note describes a simple technique, the Gamma (or Near Neighbour) test, which in many cases can be used to considerably simplify the design process of constructing a smooth data model such as a neural network. The Gamma test is a data analysis routine, that (in an optimal implementation) runs in time O(MlogM)as M→∞,where Mis the number of sample data points, and which aims to estimate the best Mean Squared Error (MSError) that can be achieved by any continuous or smooth (bounded first partial derivatives) data model constructed using the data.
3067787
Neuro-genetic adaptive attitude control
It has previously been demonstrated that for smooth dynamic systems, using relatively few sample points from a single trajectory, a neural network can be trained to perform very accurate short-term prediction over a large part of the phase space. In this paper, we exploit the capability of a Locally Predictive Network (LPN) to derive an adaptive control architecture for a satellite equipped with controllable, bidirectional thrusters on each of the three principal axes. It is assumed that a hardware implementation of the neural network is available. The inputs for the network are a small history of system states up to the present time and a set of current control inputs, the outputs are the next system state. Once the LPN has been trained successfully, at each time step a genetic algorithm searches the space of hypothetical control inputs. Given a set of control signals, the LPN is used to predict the state of the system at the next sample point. This enables the ‘fitness’ of each set of hypothetical control torques to be evaluated very rapidly. In effect, the genetic algorithm determines a satisfactory solution to the inverse kinematic problem in time to apply the solution (set of control torques) at the next control point. With the exception of the neuromodelling (which is repeated only when the system dynamics change), the whole process is then repeated. The results presented indicate that such an architecture is easily able to master the attitude control problem for arbitrary slew angles, with arbitrary a priori unknowndynamics and noise in the sensor system.
206639466
Neural Networks and Genetic Algorithms for the Attitude Control Problem
A general adaptive control method using genetic algorithms and neural networks is proposed and applied to a highly nonlinear problem, the attitude control problem. Examples are given where the method successfully control a rigid body satellite with unknown dynamics, including an example where the satellite is subject to external forces trying to lead it into a chaotic motion.
20473212
Towards a formal theory of defeasible deontic conditionals
The paper outlines the approach to the analysis of deontic conditionals taken in earlier work of Jones and Pörn, comparing it briefly with two main trends within dyadic deontic logic, and discussing problems associated with the augmentation principle and the factual detachment principle. A modification of the Jones and Pörn system is then presented, using a classical but not normal (in the sense of Chellas) deontic modality, to provide the basis for an alternative analysis of deontic conditionals. This new analysis validates neither the factual detachment nor the augmentation principles. However, influenced by the approach of Delgrande to default reasoning, it is shown how a restricted form of factual detachment might be accommodated within the revised system.
47276
A multi-processing software infrastructure for robotic systems
Robotic systems and their software design are based on the same principles found in other computer applications. However, while many computer systems to date are deeply rooted in concepts such as multi-processing, multi-threads, modularity, etc., robotic systems are still limited by mono-processing, centralized designs. The paper addresses these limitations by proposing a new philosophy for robotic system software design - software infrastructure $that allows for the design of an efficient, modular, fault-tolerant, and distributed software architecture. Two examples of this infrastructure applied to mobile robot navigation are also discussed.
4157117
Data Access and Integration in the ISPIDER Proteomics Grid
Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources.
9096556
A language extension for expressing constraints on data access
Controlled sharing of information is needed and desirable for many applications and is supported in operating systems by access control mechanisms. This paper shows how to extend programming languages to provide controlled sharing. The extension permits expression of access constraints on shared data. Access constraints can apply both to simple objects, and to objects that are components of larger objects, such as bank account records in a bank's data base. The constraints are stated declaratively, and can be enforced by static checking similar to type checking. The approach can be used to extend any strongly-typed language, but is particularly suitable for extending languages that support the notion of abstract data types.
3048144
An analysis of extensible modelling for functional genomics data
BackgroundSeveral data formats have been developed for large scale biological experiments, using a variety of methodologies. Most data formats contain a mechanism for allowing extensions to encode unanticipated data types. Extensions to data formats are important because the experimental methodologies tend to be fairly diverse and rapidly evolving, which hinders the creation of formats that will be stable over time.ResultsIn this paper we review the data formats that exist in functional genomics, some of which have become de facto or de jure standards, with a particular focus on how each domain has been modelled, and how each format allows extensions. We describe the tasks that are frequently performed over data formats and analyse how well each task is supported by a particular modelling structure.ConclusionFrom our analysis, we make recommendations as to the types of modelling structure that are most suitable for particular types of experimental annotation. There are several standards currently under development that we believe could benefit from systematically following a set of guidelines.