14418558
Adaptive neuro-genetic control of chaos applied to the attitude control problem
Conventional adaptive control techniques have, for the most part, been based on methods for linear or weakly non-linear systems. More recently, neural network and genetic algorithm controllers have started to be applied to complex, non-linear dynamic systems. The control of chaotic dynamic systems poses a series of especially challenging problems. In this paper, an adaptive control architecture using neural networks and genetic algorithms is applied to a complex, highly nonlinear, chaotic dynamic system: the adaptive attitude control problem (for a satellite), in the presence of large, external forces (which left to themselves led the system into a chaotic motion). In contrast to the OGY method, which uses small control adjustments to stabilize a chaotic system in an otherwise unstable but natural periodic orbit of the system, the neuro-genetic controller may use large control adjustments and proves capable of effectively attaining any specified system state, with no a prioriknowledge of the dynamics, even in the presence of significant noise.
14346877
The Construction of Smooth Models using Irregular Embeddings Determined by a Gamma Test Analysis
One of the key problems in forming a smooth model from input-output data is the determination of which input variables are relevant in predicting a given output. In this paper, we show how the Gamma test can be used to select that combination of input variables which can best be employed to form a smooth model of an output. For time series prediction this amounts to the selection of an appropriate irregular embedding. We give some simple zero noise examples of time series analysis, and illustrate how using these techniques a binary message encoded into a chaotic carrier can be retrieved without knowledge of the dynamics used to generate the carrier. Provided the underlying dynamics are such as to produce a smooth embedding model with bounded partial derivatives, the sampling distribution is dense in input space, and any associated distribution of measurement error has the first few moments bounded, so that the typical prerequisite conditions of the Gamma test are satisfied, we conclude that the Gamma test is an effective tool in the determination of irregular time series embeddings. These techniques can also be useful in practical applications which involve filtering seismic data to detect anomalous events.
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.
172513
Comparing networks with differing neural-node functions using transputer-based genetic algorithms
Different neural net node computational functions are compared using feedforward backpropagation networks. Three node types are examined: the standard model, ellipsoidal nodes and quadratic nodes. After preliminary experiments on simple small problems, in which quadratic nodes performed very well, networks of differing nodes types are applied to the speech recognition 104 speaker E-task using a fixed architecture. Ellipsoidal nodes were found to work well, but not as well as the standard model. Quadratic nodes did not perform well on the larger task. To facilitate an architecture independent comparison a transputer-based genetic algorithm is then used to compare ellipsoidal and mixed ellipsoidal and standard networks with the standard model. These experiments confirmed the earlier conclusion that ellipsoidal networks could not compare favourably with the standard model on the 104 speaker E-task. In an evolutionary search in which layer node types were free to adjust ellipsoidal nodes had a tendency to become extinct or barely survive. If the presence of ellipsoidal nodes was enforced then the resulting networks again performed poorly when compared with the standard model.
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.
3022268
Feature selection for genetic sequence classification
MOTIVATION
Most of the existing methods for genetic sequence classification are based on a computer search for homologies in nucleotide or amino acid sequences. The standard sequence alignment programs scale very poorly as the number of sequences increases or the degree of sequence identity is <30%. Some new computationally inexpensive methods based on nucleotide or amino acid compositional analysis have been proposed, but prediction results are still unsatisfactory and depend on the features chosen to represent the sequences.


RESULTS
In this paper, a feature selection method based on the Gamma (or near-neighbour) test is proposed. If there is a continuous or smooth map from feature space to the classification target values, the Gamma test gives an estimate for the mean-squared error of the classification, despite the fact that one has no a priori knowledge of the smooth mapping. We can search a large space of possible feature combinations for a combination which gives a smallest estimated mean-squared error using a genetic algorithm. The method was used for feature selection and classification of the large subunits of rRNA according to RDP (Ribosomal Database Project) phylogenetic classes. The sequences were represented by dinucleotide frequency distribution. The nearest-neighbour criterion has been used to estimate the predictive accuracy of the classification based on the selected features. For examples discussed, we found that the classification according to the first nearest neighbour is correct for 80% of the test samples. If we consider the set of the 10 nearest neighbours, then 94% of the test samples are classified correctly.


AVAILABILITY
The principal novel component of this method is the Gamma test and this can be downloaded compiled for Unix Sun 4, Windows 95 and MS-DOS from http://www.cs.cf.ac.uk/ec/


CONTACT
s.margetts@cs.cf.ac.uk
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.
33714344
Editorial note
206840293
Formal Specification of Security Requirements using the Theory of Normative Positions
We use a number of the examples presented in [Ting 1990] to illustrate how the formal theory of normative positions may serve as a tool for clarifying, and making precise, the specification of security requirements, particularly in regard to access control. We describe the basic features of the theory of normative positions (which has its roots in the analytical theory of law), and of the modal logics (deontic and action logics) involved in its formulation. We then indicate three levels of software we have under development, which aim to turn the analytical procedures into a practical tool. Our concluding remarks relate our discussion of Ting's examples to some particular issues in the formal specification of computer systems.
16685816
An object model and database for functional genomics
MOTIVATION
Large-scale functional genomics analysis is now feasible and presents significant challenges in data analysis, storage and querying. Data standards are required to enable the development of public data repositories and to improve data sharing. There is an established data format for microarrays (microarray gene expression markup language, MAGE-ML) and a draft standard for proteomics (PEDRo). We believe that all types of functional genomics experiments should be annotated in a consistent manner, and we hope to open up new ways of comparing multiple datasets used in functional genomics.


RESULTS
We have created a functional genomics experiment object model (FGE-OM), developed from the microarray model, MAGE-OM and two models for proteomics, PEDRo and our own model (Gla-PSI-Glasgow Proposal for the Proteomics Standards Initiative). FGE-OM comprises three namespaces representing (i) the parts of the model common to all functional genomics experiments; (ii) microarray-specific components; and (iii) proteomics-specific components. We believe that FGE-OM should initiate discussion about the contents and structure of the next version of MAGE and the future of proteomics standards. A prototype database called RNA And Protein Abundance Database (RAPAD), based on FGE-OM, has been implemented and populated with data from microbial pathogenesis.


AVAILABILITY
FGE-OM and the RAPAD schema are available from http://www.gusdb.org/fge.html, along with a set of more detailed diagrams. RAPAD can be accessed by registration at the site.
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.