Higher order neural networks for financial time series prediction

Neural networks have been shown to be a promising tool for forecasting financial times series. Numerous research and applications of neural networks in business have proven their advantage in relation to classical methods that do not include artificial intelligence. What makes this particular use of neural networks so attractive to financial analysts and traders is the fact that governments and companies benefit from it to make decisions on investment and trading. However, when the number of inputs to the model and the number of training examples becomes extremely large, the training procedure for ordinary neural network architectures becomes tremendously slow and unduly tedious. To overcome such time-consuming operations, this research work focuses on using various Higher Order Neural Networks (HONNs) which have a single layer of learnable weights, therefore reducing the networks' complexity. In order to predict the upcoming trends of univariate financial time series signals, three HONNs models; the Pi-Sigma Neural Network, the Functional Link Neural Network, and the Ridge Polynomial Neural Network were used, as well as the Multilayer Perceptron. Furthermore, a novel neural network architecture which comprises of a feedback connection in addition to the feedforward Ridge Polynomial Neural Network was constructed. The proposed network combines the properties of both higher order and recurrent neural networks, and is called Dynamic Ridge Polynomial Neural Network (DRPNN). Extensive simulations covering ten financial time series were performed. The forecasting performance of various feedforward HONNs models, the Multilayer Perceptron and the novel DRPNN was compared. Simulation results indicate that HONNs, particularly the DRPNN in most cases demonstrated advantages in capturing chaotic movement in the financial signals with an improvement in the profit return over other network models. The relative superiority of DRPNN to other networks is not just its ability to attain high profit return, but rather to model the training set with fast learning and convergence. The network offers fast training and shows considerable promise as a forecasting tool. It is concluded that DRPNN do have the capability to forecast the financial markets, and individual investor could benefit from the use of this forecasting

The application of artificial neural networks to interpret acoustic emissions from submerged arc welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Automated fusion welding processes play a fundamental role in modern manufacturing industries. The proliferation of joint geometries together with the large permutation of associated process variable configurations has given rise to research into complex system modelling and control strategies. Many of these techniques have involved monitoring of not only the electrical characteristics of the process but visual and acoustic information. Acoustic information derived from certain welding processes is well documented as it is an established fact that skilled manual welders utilise such information as an aid to creating an optimum weld. The experimental investigation presented in this thesis is dedicated to the feasibility of monitoring airborne acoustic emissions of Submerged Arc Welding (SAW) for diagnostic and real time control purposes. The experimental method adopted for this research takes a cybernetic approach to data processing and interpretation in an attempt to replicate the robustness of human biological functions. A custom designed audio hardware system was used to analyse signals obtained from bead on mild steel plate fusion welds. Time and frequency domains were used in an attempt to establish salient characteristics or identify the signatures associated with changes of the process variables. The featured parameters were voltage / current and weld travel speed, due to their ease of validation. However, consideration has also been given to weld defect prediction due to process instabilities. As the data proved to be highly correlated and erratic when subjected to off line statistical analysis, extensive investigation was given to the application of artificial neural networks to signal processing and real time control scenarios. As a consequence, a dedicated neural based software system was developed, utilising supervised and unsupervised neural techniques to monitor the process. The research was aimed at proving the feasibility of monitoring the electrical process parameters and stability of the welding process in real time. It was shown to be possible, by the exploitation of artificial neural networks, to generate a number of monitoring parameters indicative of the welding process state. The limitations of the present neural method and proposed developments are discussed, together with an overview of applied neural network technology and its impact on artificial intelligence and robotic control. Further developments are considered together with recommendations for future areas of research

Image database retrieval using neural networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The broad objective of this work has been to achieve retrieval of images from large unconstrained databases using image content. The problem is typified by the need to locate a target image within a database where no numerical indexing terms exist. Here, retrieval is based on important features within in an image and uses sample images or user sketches to specify a query. A typical query might be framed as "Find all images similar to this one", for example. The aim of this work has been to show how neural networks can provide a practical, flexible and robust solution to this problem. A neural network is basically an adaptive information filter which can be used to extract the salient characteristics of a data set during a training phase. The transformation learnt by the network can map the images into compact indices which support very rapid fuzzy matching of images across the database. This learning process optimises the performance of the code with respect to the contents of the database. We assess the applicability of several neural network architectures and learning rules for a practical coding scheme and investigate how the system parameters affect the performance of the system. We introduce a novel learning law which has a number of advantages over existing paradigms. In-depth mathematical analysis and extensive empirical tests are used to corroborate the arguments presented throughout. This thesis aims to show the nature of the image retrieval problem, how current research trends attempt to tackle it and how neural networks can offer us a real alternative to conventional approaches

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

The 1991 3rd NASA Symposium on VLSI Design

Papers from the symposium are presented from the following sessions: (1) featured presentations 1; (2) very large scale integration (VLSI) circuit design; (3) VLSI architecture 1; (4) featured presentations 2; (5) neural networks; (6) VLSI architectures 2; (7) featured presentations 3; (8) verification 1; (9) analog design; (10) verification 2; (11) design innovations 1; (12) asynchronous design; and (13) design innovations 2

Wet Gas Flow Metering with Pattern Recognition Techniques

The development of many gas condensate fields and the increasing number of marginal fields whose economics do not support conventional bulky separation and processing facilities means that new wet gas flow metering techniques are becoming of greater importance to the oil and gas industry worldwide. For the purpose of this research wet gas flow is defined as multiphase flow (gas-liquid) having in-situ gas volume fraction greater than 95 % at the point of measurement. This research presents a novel wet gas measurement technique involving the use of a standard Venturi meter together with advanced pattern (PR) recognition methods for the detection of liquid presence in wet gas flow conditions and the simultaneous measurement of gas and liquid flow rates without the need for preconditioning of the flow or prior knowledge of either phase. The technique involves four major steps: 1) collection of experimental data spanning the range of flow regimes likely to be encountered in wet gas flow conditions; 2) extraction of flow dependent variables from the Venturi pressure sensors in the form of features; 3) development of PR model for mapping between input features and corresponding gas and liquid flow rates; 4) generalisation test to new and previously unseen flow conditions to determine the accuracy of the Venturi-PR methods developed in this research work. Data was sampled at 50 Hz using two axial differential pressure sensors and one singleend absolute pressure sensor on a 2-inch horizontally mounted Venturi meter using airwater at normally atmospheric conditions. Extensive features were extracted from the time and frequency domains of the raw data and evaluated for their discriminatory ability between different flow conditions. A Bayesian multi layer perceptron (MLP) neural network was used to construct a non-linear mapping between the different feature vectors and the corresponding gas and liquid flow rates using a correctly labelled training data. When the generalisation performance of different measurement scenarios developed was tested, the cross-sensor data fusion of the amplitude features achieved 100 % of the test data to within ± 5 % error across the whole flow domain of interest. The Venturi-PR results also performed significantly better than published wet gas differential pressure flow correlations over the flow domain of interest.Ph

3D Object Recognition Based On Constrained 2D Views

The aim of the present work was to build a novel 3D object recognition system capable of classifying man-made and natural objects based on single 2D views. The approach to this problem has been one motivated by recent theories on biological vision and multiresolution analysis. The project's objectives were the implementation of a system that is able to deal with simple 3D scenes and constitutes an engineering solution to the problem of 3D object recognition, allowing the proposed recognition system to operate in a practically acceptable time frame. The developed system takes further the work on automatic classification of marine phytoplank- (ons, carried out at the Centre for Intelligent Systems, University of Plymouth. The thesis discusses the main theoretical issues that prompted the fundamental system design options. The principles and the implementation of the coarse data channels used in the system are described. A new multiresolution representation of 2D views is presented, which provides the classifier module of the system with coarse-coded descriptions of the scale-space distribution of potentially interesting features. A multiresolution analysis-based mechanism is proposed, which directs the system's attention towards potentially salient features. Unsupervised similarity-based feature grouping is introduced, which is used in coarse data channels to yield feature signatures that are not spatially coherent and provide the classifier module with salient descriptions of object views. A simple texture descriptor is described, which is based on properties of a special wavelet transform. The system has been tested on computer-generated and natural image data sets, in conditions where the inter-object similarity was monitored and quantitatively assessed by human subjects, or the analysed objects were very similar and their discrimination constituted a difficult task even for human experts. The validity of the above described approaches has been proven. The studies conducted with various statistical and artificial neural network-based classifiers have shown that the system is able to perform well in all of the above mentioned situations. These investigations also made possible to take further and generalise a number of important conclusions drawn during previous work carried out in the field of 2D shape (plankton) recognition, regarding the behaviour of multiple coarse data channels-based pattern recognition systems and various classifier architectures. The system possesses the ability of dealing with difficult field-collected images of objects and the techniques employed by its component modules make possible its extension to the domain of complex multiple-object 3D scene recognition. The system is expected to find immediate applicability in the field of marine biota classification