Modified Hopfield Neural Network Classification Algorithm For Satellite Images

Air adalah bahan yang penting bagi kehidupan mahkluk di atas muka bumi ini. Aktiviti manusia dan pengaruh alam semula jadi memberi kesan terhadap kualiti air, dan ia dianggap satu daripada masalah terbesar yang membelenggui kehidupan. Water is an essential material for living creatures. Human activities and natural influences have an effecting on water quality, and this is considered one of the largest problems facing living forms

Formal concept matching and reinforcement learning in adaptive information retrieval

The superiority of the human brain in information retrieval (IR) tasks seems to come firstly from its ability to read and understand the concepts, ideas or meanings central to documents, in order to reason out the usefulness of documents to information needs, and secondly from its ability to learn from experience and be adaptive to the environment. In this work we attempt to incorporate these properties into the development of an IR model to improve document retrieval. We investigate the applicability of concept lattices, which are based on the theory of Formal Concept Analysis (FCA), to the representation of documents. This allows the use of more elegant representation units, as opposed to keywords, in order to better capture concepts/ideas expressed in natural language text. We also investigate the use of a reinforcement leaming strategy to learn and improve document representations, based on the information present in query statements and user relevance feedback. Features or concepts of each document/query, formulated using FCA, are weighted separately with respect to the documents they are in, and organised into separate concept lattices according to a subsumption relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the concepts in the lattice representation. This avoids implementation drawbacks faced by other FCA-based approaches. Retrieval of a document for an information need is based on concept matching between concept lattice representations of a document and a query. The learning strategy works by making the similarity of relevant documents stronger and non-relevant documents weaker for each query, depending on the relevance judgements of the users on retrieved documents. Our approach is radically different to existing FCA-based approaches in the following respects: concept formulation; weight assignment to object-attribute pairs; the representation of each document in a separate concept lattice; and encoding concept lattices in BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our learning strategy makes use of relevance feedback information to enhance document representations, thus making the document representations dynamic and adaptive to the user interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are presented and compared with published results. In particular, the performance of the system is shown to improve significantly as the system learns from experience.The School of Computing, University of Plymouth, UK

Power System Load Modeling Using A Weighted Optimal Linear Associative Memory (Olam)

Power system load models are very powerful tools, which have a wide range of applications in the electric power industry. These uses include scheduling system maintenance, monitoring load management policies, helping with the generator commitment problem by providing short-term forecasts, and aiding system planning [4]. Further, Power System Load Modeling is a technique used to model a power system and other essentials for the assessment of stability. In today’s datacenters, power consumption is a major issue. Storage usually typically comprises a large percentage of a datacenter’s power. Therefore, without mentioning that managing, understanding, and reducing storage, power consumption is an essential aspect of any efforts that address the total power consumption of datacenters. Moreover, according to [16], power system load models have a wide range of applications in the electric power industry including load management policy monitoring, such as aiding with system planning by providing long-term forecasts, short-term forecasts, and others including assisting with the generator commitment problem

Synaptic plasticity and memory addressing in biological and artificial neural networks

Biological brains are composed of neurons, interconnected by synapses to create large complex networks. Learning and memory occur, in large part, due to synaptic plasticity -- modifications in the efficacy of information transmission through these synaptic connections. Artificial neural networks model these with neural "units" which communicate through synaptic weights. Models of learning and memory propose synaptic plasticity rules that describe and predict the weight modifications. An equally important but under-evaluated question is the selection of \textit{which} synapses should be updated in response to a memory event. In this work, we attempt to separate the questions of synaptic plasticity from that of memory addressing. Chapter 1 provides an overview of the problem of memory addressing and a summary of the solutions that have been considered in computational neuroscience and artificial intelligence, as well as those that may exist in biology. Chapter 2 presents in detail a solution to memory addressing and synaptic plasticity in the context of familiarity detection, suggesting strong feedforward weights and anti-Hebbian plasticity as the respective mechanisms. Chapter 3 proposes a model of recall, with storage performed by addressing through local third factors and neo-Hebbian plasticity, and retrieval by content-based addressing. In Chapter 4, we consider the problem of concurrent memory consolidation and memorization. Both storage and retrieval are performed by content-based addressing, but the plasticity rule itself is implemented by gradient descent, modulated according to whether an item should be stored in a distributed manner or memorized verbatim. However, the classical method for computing gradients in recurrent neural networks, backpropagation through time, is generally considered unbiological. In Chapter 5 we suggest a more realistic implementation through an approximation of recurrent backpropagation. Taken together, these results propose a number of potential mechanisms for memory storage and retrieval, each of which separates the mechanism of synaptic updating -- plasticity -- from that of synapse selection -- addressing. Explicit studies of memory addressing may find applications not only in artificial intelligence but also in biology. In artificial networks, for example, selectively updating memories in large language models can help improve user privacy and security. In biological ones, understanding memory addressing can help with health outcomes and treating memory-based illnesses such as Alzheimers or PTSD

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms

Self Organisation and Hierarchical Concept Representation in Networks of Spiking Neurons

The aim of this work is to introduce modular processing mechanisms for cortical functions implemented in networks of spiking neurons. Neural maps are a feature of cortical processing found to be generic throughout sensory cortical areas, and self-organisation to the fundamental properties of input spike trains has been shown to be an important property of cortical organisation. Additionally, oscillatory behaviour, temporal coding of information, and learning through spike timing dependent plasticity are all frequently observed in the cortex. The traditional self-organising map (SOM) algorithm attempts to capture the computational properties of this cortical self-organisation in a neural network. As such, a cognitive module for a spiking SOM using oscillations, phasic coding and STDP has been implemented. This model is capable of mapping to distributions of input data in a manner consistent with the traditional SOM algorithm, and of categorising generic input data sets. Higher-level cortical processing areas appear to feature a hierarchical category structure that is founded on a feature-based object representation. The spiking SOM model is therefore extended to facilitate input patterns in the form of sets of binary feature-object relations, such as those seen in the field of formal concept analysis. It is demonstrated that this extended model is capable of learning to represent the hierarchical conceptual structure of an input data set using the existing learning scheme. Furthermore, manipulations of network parameters allow the level of hierarchy used for either learning or recall to be adjusted, and the network is capable of learning comparable representations when trained with incomplete input patterns. Together these two modules provide related approaches to the generation of both topographic mapping and hierarchical representation of input spaces that can be potentially combined and used as the basis for advanced spiking neuron models of the learning of complex representations

Integrating the key approaches of neural networks

The thesis is written in chapter form. Chapter 1 describes some of the history of neural networks and its place in the field of artificial intelligence. It indicates the biological basis from which neural network approximation are made. Chapter 2 describes the properties of neural networks and their uses. It introduces the concepts of training and learning. Chapters 3, 4, 5 and 6 show the perceptron and adaline in feedforward and recurrent networks particular reference is made to regression substitution by "group method data handling. Networks are chosen that explain the application of neural networks in classification, association, optimization and self organization. Chapter 7 addresses the subject of practical inputs to neural networks. Chapter 8 reviews some interesting recent developments. Chapter 9 reviews some ideas on the future technology for neural networks. Chapter 10 gives a listing of some neural network types and their uses. Appendix A gives some of the ideas used in portfolio selection for the Johannesburg Stock Exchange.ComputingM. Sc. (Operations Research

Smart Adaptive Homes and Their Potential to Improve Space Efficiency and Personalisation

Over the last decades, population growth in urban areas and the subsequent rise in demand for housing have resulted in significant space and housing shortages. This paper investigates the influence of smart technologies on small urban dwellings to make them flexible, adaptive and personalised. The study builds on the hypothesis that adaptive homes and smart technology could increase efficiency and space usage up to two to three times compared to a conventional apartment. The present study encompasses a comprehensive semi-systematic literature review that includes several case studies of smart adaptive homes demonstrating various strategies that can be employed to enhance the functionality of small spaces while reducing the physical and psychological limitations associated with them. These strategies involve incorporating time-dependent functions and furniture, as well as division elements that can adapt to the changing needs of users in real-time. This review further categorises types of flexibility and adaptation regarding the size of the moving elements, the time that the transformation takes and whether it is performed manually (by a human) or automatically (by a machine). Results show that smart and adaptive technology can increase space efficiency by reducing the need for separate physical spaces for different activities. Smart technology substantially increases the versatility and multifunctionality of a room in all three dimensions and allows for adaptation and customisation for a variety of users