An associative memory for the on-line recognition and prediction of temporal sequences

This paper presents the design of an associative memory with feedback that is capable of on-line temporal sequence learning. A framework for on-line sequence learning has been proposed, and different sequence learning models have been analysed according to this framework. The network model is an associative memory with a separate store for the sequence context of a symbol. A sparse distributed memory is used to gain scalability. The context store combines the functionality of a neural layer with a shift register. The sensitivity of the machine to the sequence context is controllable, resulting in different characteristic behaviours. The model can store and predict on-line sequences of various types and length. Numerical simulations on the model have been carried out to determine its properties.Comment: Published in IJCNN 2005, Montreal, Canad

Comparison of Bidirectional Associative Memory, Counterpropagation and Evolutionary Neural Network for Java Characters Recognition

Javanese language is the language used by the people on the island of Java and it has its own form of letters called Java characters. Recognition of Java characters is quite difficult because it consist of basic characters, numbers, complementary characters, and so on. In this research we developed a system to recognize Java characters and compared three methods of neural network namely bidirectional associative memory, counterpropagation and evolutionary neural network. Input for the system is a digital image containing several Java characters. Digital image processing and segmentation are performed on the input image to get each Java character. For each Java character, feature extraction is done using ICZ-ZCZ method. Output from feature extraction will become input for neural network. From experimental result, evolutionary neural network can perform better recognition accuracy than the other two methods

Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

Javanese characters are traditional characters that are used to write the Javanese language. The Javanese language is a language used by many people on the island of Java, Indonesia. The use of Javanese characters is diminishing more and more because of the difficulty of studying the Javanese characters themselves. The Javanese character set consists of basic characters, numbers, complementary characters, and so on. In this research we have developed a system to recognize Javanese characters. Input for the system is a digital image containing several handwritten Javanese characters. Preprocessing and segmentation are performed on the input image to get each character. For each character, feature extraction is done using the ICZ-ZCZ method. The output from feature extraction will become input for an artificial neural network. We used several artificial neural networks, namely a bidirectional associative memory network, a counterpropagation network, an evolutionary network, a backpropagation network, and a backpropagation network combined with chi2. From the experimental results it can be seen that the combination of chi2 and backpropagation achieved better recognition accuracy than the other methods

The relaxation method for learning in artificial neural networks

A new mathematical approach for deriving learning algorithms for various neural network models including the Hopfield model, Bidirectional Associative Memory, Dynamic Heteroassociative Neural Memory, and Radial Basis Function Networks is presented. The mathematical approach is based on the relaxation method for solving systems of linear inequalities. The newly developed learning algorithms are fast and they guarantee convergence to a solution in a finite number of steps. The new algorithms are highly insensitive to choice of parameters and the initial set of weights. They also exhibit high scalability on binary random patterns. Rigorous mathematical foundations for the new algorithms and their simulation studies are included

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

The Brain is a Suitability Probability Processor: A macro model of our neural control system

Our world is characterized by growing diversity and complexity, and the effort to manage our affairs in a good way becomes increasingly difficult. This is true for all spheres of life, including culture, economy, technology, science, politics, environment and daily grind. A corresponding development occurs to our understanding of the brain, which is the crucial organ to keep track of everything. The amount of domain specific findings about this organ grows dramatically, what takes preferably place by highly specialized research. But the holistic understanding of the brain is rather more challenged than supported by this development, resulting in a huge lack of knowledge on the systemic level of the neurosciences. Eckhard Schindler faces this dilemma by introducing a macro model of the brain. This is not only an attempt to improve the perception of our most crucial organ, but also to open a door for a better understanding of our species and for ease our life again.:Part 1 - The Brain as Suitability Probability Processor Introduction Neuro basics Purpose, perception and motor control Excitation, inhibition, pattern transformation and circuits Memory Homeostasis, pain, emotions and rewards The SPP model The emoti(onal-moti)vational system The control levels of the central nervous system The attention assessment controller (AAC) Efficiency through delegation and structuring Universal suitability probability evaluation Needs and library of associative-emotivational patterns Higher needs Needs and suitability probability evaluation Suitability probability evaluation and evolution The two types of consciousness Conscious experiences Individual and social consciousness The 4DI model A four-dimensional intelligence concept (4DI) Dynamics of the need hierarchy Social emotivational dependency chains The need for coherence Artificial needs versus growth needs Dynamics in the 3D tension field 3D tensions in the affluent society The tunnel vision paradox Emotivational amplification adaptation Fading consciousness in affluent contexts About the integrative ingredient of 4DI Toe-holds for other disciplines Part 2 - Excursions to the current state of science Introduction Basal ganglia (BG) and frontal cortex Emotion, motivation and memory Cognitive control and emotions Consciousness Psychology Brain and computer The biggest open questions Index of figures Index of tables Reference

Design for novel enhanced weightless neural network and multi-classifier.

Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems. A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN. Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems