Neural Distributed Autoassociative Memories: A Survey

Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension. The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons). Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints. Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.Comment: 31 page

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

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

Learning as a Nonlinear Line of Attraction for Pattern Association, Classification and Recognition

Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns. It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter this picture. That is, if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented by an unknown encoded representation of a visual image. The conception of the dynamics of the nonlinear line attractor network to operate between stable and unstable states is the second contribution in this dissertation research. These criteria can be used to circumvent the plasticity-stability dilemma by using the unstable state as an indicator to create a new line for an unfamiliar pattern. This novel learning strategy utilizes stability (convergence) and instability (divergence) criteria of the designed dynamics to induce self-organizing behavior. The self-organizing behavior of the nonlinear line attractor model can manifest complex dynamics in an unsupervised manner. The third contribution of this dissertation is the introduction of the concept of manifold of color perception. The fourth contribution of this dissertation is the development of a nonlinear dimensionality reduction technique by embedding a set of related observations into a low-dimensional space utilizing the result attained by the learned memory matrices of the nonlinear line attractor network. Development of a system for affective states computation is also presented in this dissertation. This system is capable of extracting the user\u27s mental state in real time using a low cost computer. It is successfully interfaced with an advanced learning environment for human-computer interaction

New Learning and Control Algorithms for Neural Networks.

Neural networks offer distributed processing power, error correcting capability and structural simplicity of the basic computing element. Neural networks have been found to be attractive for applications such as associative memory, robotics, image processing, speech understanding and optimization. Neural networks are self-adaptive systems that try to configure themselves to store new information. This dissertation investigates two approaches to improve performance: better learning and supervisory control. A new learning algorithm called the Correlation Continuous Unlearning (CCU) algorithm is presented. It is based on the idea of removing undesirable information that is encountered during the learning period. The control methods proposed in the dissertation improve the convergence by affecting the order of updates using a controller. Most previous studies have focused on monolithic structures. But it is known that the human brain has a bicameral nature at the gross level and it also has several specialized structures. In this dissertation, we investigate the computing characteristics of neural networks that are not monolithic being enhanced by a controller that can run algorithms that take advantage of the known global characteristics of the stored information. Such networks have been called bicameral neural networks. Stinson and Kak considered elementary bicameral models that used asynchronous control. New control methods, the method of iteration and bicameral classifier, are now proposed. The method of iteration uses the Hamming distance between the probe and the answer to control the convergence to a correct answer, whereas the bicameral classifier takes advantage of global characteristics using a clustering algorithm. The bicameral classifier is applied to two different models of equiprobable patterns as well as the more realistic situation where patterns can have different probabilities. The CCU algorithm has also been applied to a bidirectional associative memory with greatly improved performance. For multilayered networks, indexing of patterns to enhance system performance has been studied