PhysicsGP: A Genetic Programming Approach to Event Selection

We present a novel multivariate classification technique based on Genetic Programming. The technique is distinct from Genetic Algorithms and offers several advantages compared to Neural Networks and Support Vector Machines. The technique optimizes a set of human-readable classifiers with respect to some user-defined performance measure. We calculate the Vapnik-Chervonenkis dimension of this class of learning machines and consider a practical example: the search for the Standard Model Higgs Boson at the LHC. The resulting classifier is very fast to evaluate, human-readable, and easily portable. The software may be downloaded at: http://cern.ch/~cranmer/PhysicsGP.htmlComment: 16 pages 9 figures, 1 table. Submitted to Comput. Phys. Commu

Parallelizing Feed-Forward Artificial Neural Networks on Transputers

This thesis is about parallelizing the training phase of a feed-forward, artificial neural network. More specifically, we develop and analyze a number of parallelizations of the widely used neural net learning algorithm called back-propagation. We describe two different strategies for parallelizing the back-propagation algorithm. A number of parallelizations employing these strategies have been implemented on a system of 48 transputers, permitting us to evaluate and analyze their performances based on the results of actual runs

Mechatronics of systems with undetermined configurations

This work is submitted for the award of a PhD by published works. It deals with some of the efforts of the author over the last ten years in the field of Mechatronics. Mechatronics is a new area invented by the Japanese in the late 1970's, it consists of a synthesis of computers and electronics to improve mechanical systems. To control any mechanical event three fundamental features must be brought together: the sensors used to observe the process, the control software, including the control algorithm used and thirdly the actuator that provides the stimulus to achieve the end result. Simulation, which plays such an important part in the Mechatronics process, is used in both in continuous and discrete forms. The author has spent some considerable time developing skills in all these areas. The author was certainly the first at Middlesex to appreciate the new developments in Mechatronics and their significance for manufacturing. The author was one of the first mechanical engineers to recognise the significance of the new transputer chip. This was applied to the LQG optimal control of a cinefilm copying process. A 300% improvement in operating speed was achieved, together with tension control. To make more efficient use of robots they have to be made both faster and cheaper. The author found extremely low natural frequencies of vibration, ranging from 3 to 25 Hz. This limits the speed of response of existing robots. The vibration data was some of the earliest available in this field, certainly in the UK. Several schemes have been devised to control the flexible robot and maintain the required precision. Actuator technology is one area where mechatronic systems have been the subject of intense development. At Middlesex we have improved on the Aexator pneumatic muscle actuator, enabling it to be used with a precision of about 2 mm. New control challenges have been undertaken now in the field of machine tool chatter and the prevention of slip. A variety of novel and traditional control algorithms have been investigated in order to find out the best approach to solve this problem

From Conventional to Cl-Based Spatial Analysis

Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Autonomy in the real real-world: A behaviour based view of autonomous systems control in an industrial product inspection system

The thesis presented in this dissertation appears in two sequential parts that arose from an exploration of the use of Behaviour Based Artificial Intelligence (BBAI) techniques in a domain outside that of robotics, where BBAI is most frequently used. The work details a real-world physical implementation of the control and interactions of an industrial product inspection system from a BBAI perspective. It concentrates particularly on the control of a number of active laser scanning sensor systems (each a subsystem of a larger main inspection system), using a subsumption architecture. This industrial implementation is in itself a new direction for BBAI control and an important aspect of this thesis. However, the work has also led on to the development of a number of key ideas which contribute to the field of BBAI in general. The second part of the thesis concerns the nature of physical and temporal constraints on a distributed control system and the desirability of utilising mechanisms to provide continuous, low-level learning and adaptation of domain knowledge on a sub-behavioural basis. Techniques used include artificial neural networks and hill-climbing state-space search algorithms. Discussion is supported with examples from experiments with the laser scanning inspection system. Encouraging results suggest that concerted design effort at this low level of activity will benefit the whole system in terms of behavioural robustness and reliability. Relevant aspects of the design process that should be of value in similar real-world projects are identified and emphasised. These issues are particularly important in providing a firm foundation for artificial intelligence based control systems

Efficient Mapping of Neural Network Models on a Class of Parallel Architectures.

This dissertation develops a formal and systematic methodology for efficient mapping of several contemporary artificial neural network (ANN) models on k-ary n-cube parallel architectures (KNC\u27s). We apply the general mapping to several important ANN models including feedforward ANN\u27s trained with backpropagation algorithm, radial basis function networks, cascade correlation learning, and adaptive resonance theory networks. Our approach utilizes a parallel task graph representing concurrent operations of the ANN model during training. The mapping of the ANN is performed in two steps. First, the parallel task graph of the ANN is mapped to a virtual KNC of compatible dimensionality. This involves decomposing each operation into its atomic tasks. Second, the dimensionality of the virtual KNC architecture is recursively reduced through a sequence of transformations until a desired metric is optimized. We refer to this process as folding the virtual architecture. The optimization criteria we consider in this dissertation are defined in terms of the iteration time of the algorithm on the folded architecture. If necessary, the mapping scheme may utilize a subset of the processors of a given KNC architecture if it results in the most efficient simulation. A unique feature of our mapping is that it systematically selects an appropriate degree of parallelism leading to a highly efficient realization of the ANN model on KNC architectures. A novel feature of our work is its ability to efficiently map unit-allocating ANN\u27s. These networks possess a dynamic structure which grows during training. We present a highly efficient scheme for simulating such networks on existing KNC parallel architectures. We assume an upper bound on size of the neural network We perform the folding such that the iteration time of the largest network is minimized. We show that our mapping leads to near-optimal simulation of smaller instances of the neural network. In addition, based on our mapping no data migration or task rescheduling is needed as the size of network grows

Applications of neural networks to control systems

Tese de dout., Engenharia Electrónica, School of Electronic Engineering Science, Univ. of Wales, Bangor, 1992This work investigates the applicability of artificial neural networks to control systems. The following properties of neural networks are identified as of major interest to this field: their ability to implement nonlinear mappings, their massively parallel structure and their capacity to adapt. Exploiting the first feature, a new method is proposed for PID autotuning. Based on integral measures of the open or closed loop step response, multilayer perceptrons (MLPs) are used to supply PID parameter values to a standard PID controller. Before being used on-line, the MLPs are trained offline, to provide PID parameter values based on integral performance criteria. Off-line simulations, where a plant with time-varying parameters and time varying transfer function is considered, show that well damped responses are obtained. The neural PID autotuner is subsequently implemented in real-time. Extensive experimentation confirms the good results obtained in the off-line simulations. To reduce the training time incurred when using the error back-propagation algorithm, three possibilities are investigated. A comparative study of higherorder methods of optimization identifies the Levenberg-Marquardt (LM)algorithm as the best method. When used for function approximation purposes, the neurons in the output layer of the MLPs have a linear activation function. Exploiting this linearity, the standard training criterion can be replaced by a new, yet equivalent, criterion. Using the LM algorithm to minimize this new criterion, together with an alternative form of Jacobian matrix, a new learning algorithm is obtained. This algorithm is subsequently parallelized. Its main blocks of computation are identified, separately parallelized, and finally connected together. The training time of MLPs is reduced by a factor greater than 70 executing the new learning algorithm on 7 Inmos transputers

An instruction systolic array architecture for multiple neural network types

Modern electronic systems, especially sensor and imaging systems, are beginning to incorporate their own neural network subsystems. In order for these neural systems to learn in real-time they must be implemented using VLSI technology, with as much of the learning processes incorporated on-chip as is possible. The majority of current VLSI implementations literally implement a series of neural processing cells, which can be connected together in an arbitrary fashion. Many do not perform the entire neural learning process on-chip, instead relying on other external systems to carry out part of the computation requirements of the algorithm. The work presented here utilises two dimensional instruction systolic arrays in an attempt to define a general neural architecture which is closer to the biological basis of neural networks - it is the synapses themselves, rather than the neurons, that have dedicated processing units. A unified architecture is described which can be programmed at the microcode level in order to facilitate the processing of multiple neural network types. An essential part of neural network processing is the neuron activation function, which can range from a sequential algorithm to a discrete mathematical expression. The architecture presented can easily carry out the sequential functions, and introduces a fast method of mathematical approximation for the more complex functions. This can be evaluated on-chip, thus implementing the entire neural process within a single system. VHDL circuit descriptions for the chip have been generated, and the systolic processing algorithms and associated microcode instruction set for three different neural paradigms have been designed. A software simulator of the architecture has been written, giving results for several common applications in the field