Cellular associative neural networks for pattern recognition

Cellular Automata, 2002

This paper reports the design of a Cellular Automata Machine (CAM) to ad-dress the problem of Pattern Recognition. The design is based on an elegant computing model of a particular class of Cellular Automata (CA) referred to as Generalized Multiple Attractor CA (GMACA). The ...

A novel algorithm for unsupervised classification of datasets made up of integer valued patterns by means of Cellular Neural Network (CNN) is proposed. The adopted CNN is n-dimensional and is based on a space-variant template - neighborhood order 1 - to cluster n-dimensional datasets. The choice of a CNN architecture allows a straightforward hardware implementation, particularly suited for bi-dimensional patterns.

—The non textual image recognition is one of the important aspects of the multimedia. The image recognition is also termed as the computer vision and is used in the various areas and devices such as robotics. The ANN (Artificial Neural Networks) is one of the great tools involved in the image recognition. So far, the ANN were used in applications involving the computer vision. Although the artificial neural networks based systems are distinguished for their ability to cope with problems in pattern recognition and computer vision in general, they are rather impotent when faced with the dimensionality of problems in the image understanding domain. That makes them unable to deal sufficiently with problems such as rotation, distortion, clutter and scale variances. In this paper, we are going to deal the image recognition with the technology known as the CANN (Cellular Associative Neural Networks).The main thing which makes the difference between the traditional neural networks and the CANN is the architecture, which resembles the cellular automata. It is used in the interconnection between the various networks. We are going to have a look at the CANN technology, with the learning process and the recognition of images and the process of analysis of one dimensional and two dimensional images.

Fundamenta Informaticae, 2003

This paper presents the theory and application of a high speed, low cost pattern classifier. The proposed classifier is built around a special class of sparse network referred to as Cellular Automata (CA). A specific class of CA, termed as Multiple Attractor Cellular Automata (MACA), has been evolved through Genetic Algorithm (GA) formulation to perform the task of pattern classification. The versatility of the classification scheme is illustrated through its application in three diverse fields -data mining, image compression, and fault diagnosis. Extensive experimental results demonstrate better performance of the proposed scheme over popular classification algorithms in respect of memory overhead and retrieval time with comparable classification accuracy. Hardware architecture of the proposed classifier has been also reported.

—The non textual image recognition is one of the important aspects of the multimedia. The image recognition is also termed as the computer vision and is used in the various areas and devices such as robotics. The ANN (Artificial Neural Networks) is one of the great tools involved in the image recognition. So far, the ANN were used in applications involving the computer vision. Although the artificial neural networks based systems are distinguished for their ability to cope with problems in pattern recognition and computer vision in general, they are rather impotent when faced with the dimensionality of problems in the image understanding domain. That makes them unable to deal sufficiently with problems such as rotation, distortion, clutter and scale variances. In this paper, we are going to deal the image recognition with the technology known as the CANN (Cellular Associative Neural Networks).The main thing which makes the difference between the traditional neural networks and the CANN is the architecture, which resembles the cellular automata. It is used in the interconnection between the various networks. We are going to have a look at the CANN technology, with the learning process and the recognition of images and the process of analysis of one dimensional and two dimensional images.

2008

The Cellular Associative Neural Network (CANN) is a novel symbolic pattern matching system, currently used for both the identification of objects in noisy images and for graph similarity searching of chemical structures. Objects are defined by a set of symbolic rules, which iteratively combine low level features into higher level constructs, until object level definitions can be obtained. The flow of information follows a cellular automata based model to aid parallel implementation and rules are stored in AURA associative memories, which provide efficient storage, fast retrieval and the ability to identify partially matching rules in constant time.

Cellular Neural Networks and their Applications, …, 1996

The template coefficients (weights) of a CNN, which d l give a d e s i d p e t f o t " e , can either be found by design OT by learning.. By designw meansl thut the dccrircdfunction to be performed could be translated into a set of local dynamic rules, while "ay ICorning' is based ezclwively on pairs of input and c o n q w d n g output signals, the nlcrtioMhip of which m y be by far too complicated for the cqlicit fonnulclrion of loml rules. An ov" of design and leaming methods applicrrbk to CNNs, which sometimes att not c M y distingllishcrbk, d l be given k. Both technological constmints imposed by spec$% hadwatt implementation and pmctical constraints caused by the SpCriFc application and q d e m embedding are influencing design and leanzing. 1 Introduction Since their introduction in 1988 [l] the d k g n of both continuous-time and discretetime cellular neural networks (CT-CNNs and DT-CNNs) hae been an interding research topic. The aim is to h d a set of parameters (coefficients, synaptic weighta), which in the case of locally connected translationally invariant CNNi are usually ulled templates, so that the network perform according to a given tark. The equation for each cell c of CT-CNN is M follows: f(+) := sgn(5).

Image Processing and its Applications, 1995., Fifth …, 1995

This paper describes a novel associative processor that uses neural associative memories as its processing elements. The machine has been designed to tackle problems in AI and computer vision and using nodes that allow rapid search using inexact information over very large data sets. Each processing element is an advanced distributed associative memory (ADAM) which is capable of storing large numbers of pattern associations and allow rapid access. As a neural network, the memory performs associative match, not by conventional CAM approaches, but by forming a mapping between the patterns to be associated. The benefit of this is rapid access, fault tolerance and an ability to match on inexact data. These abilities arise due to the distributed storage used in the memory, which also allows for high capacity in the memory. The memory is designed to work on large data word sizes, i.e. matching can take place on data items as small as 64 bits or as large as 1Mb. The machine is ideally suited as a pattern processing machine, which is where most of the application work has been centered. This is because it is capable of taking large subsections of images and performing matching and comparisons on these.

2001

This paper reports a Cellular Automata (CA )model for pattern recognition.The special class of CA referred to as GMACA (Generalized Multiple Attractor Cellular Automata ),is employed to design the CA based associative memory for pattern recognition.The desired GMACA are evolved through the implementation of genetic algorithm (GA).An efficient scheme to ensure fast convergence of GA is also reported.Experimental results conform the fact that the GMACA based pattern recognizer is more powerful than the Hopfield network for memorizing arbitrary patterns.

2005 9th International Workshop on Cellular Neural Networks and Their Applications, 2005

Cellular Systems are defined by cells that have an internal state and local interactions between cells that govern the dynamics of the system. We propose to use a special kind of Cellular Neural Networks (CNNs) which operates in finite iteration discrete-time mode and mimics the processing of visual perception in biological systems for digit recognition. We propose also a solution to another type of pattern recognition problem using a non-standard cellular neural networks called Molecular Graph Networks (MGNs) which offer direct mapping from compound to property of interest such as Physico-Chemical, Toxicity, logP, Inhibitory Activity MGNs translate molecular topology to network topology. We show how to design/train by backpropagation CNNs and MGNs in their discrete-time and finite-iteration versions to perform classification on real-world data sets.

Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 2019

Data classification is a well studied problem where the aim is to identify the categories in the data based on a training set. Various machine learning methods have been utilized for the problem. On the other side, cellular automata have drawn the attention of researchers as the system provides a dynamic and a discrete model for computation. In this study a novel approach is proposed for the classification problem. The method is based on formation of classes in a cellular automata by the interaction of neighborhood cells. Initially, the training data instances are assigned to the cells of a cellular automaton. The state of a cell denotes the class assignment of that point in the instance space. At the beginning of the process, only the cells that have a data instance have class assignments. However, these class assignments are spread to the neighbor cells based on a rule inspired by the heat transfer process in nature. The experiments carried out denote that the model can identify the categories in the data and promising results have been obtained.

1999

A supervised learning algorithm for obtaining the template coe$cients in completely stable Cellular Neural Networks (CNNs) is analysed in the paper. The considered algorithm resembles the well-known perceptron learning algorithm and hence called as Recurrent Perceptron Learning Algorithm (RPLA) when applied to a dynamical network. The RPLA learns pointwise de"ned algebraic mappings from initial-state and input spaces into steady-state output space; despite learning whole trajectories through desired equilibrium points. The RPLA has been used for training CNNs to perform some image processing tasks and found to be successful in binary image processing. The edge detection templates found by RPLA have performances comparable to those of Canny's edge detector for binary images.

The Cellular Neural Networks (CNN) model is now a paradigm of ceIlular analog programmable multidimensional processor array with distributed local logic and memory. CNNs consist of many paraIlel analogue processors computing in real time. One desirable feature is that these processors arranged in a two dimensional grid, only have local connections, which lend themselves easily to VLSI implementations. The connections between these processors are determined by a cIoning template, which describes the strength of nearest-neighbour interconnections. The cloning templates are spaceinvariant, meaning that a11 the processors have the same relative connections. In this paper first we describe the architecture of CNN. Next, a new application of CNN using them for the 3D scene analysis is studied. .

2012

Data classification is a process that can categorize data to achieve the relationship between attributes and extract the suitable rules for prediction process. There are different learning methods in machine learning of which each has both advantages and disadvantages. Each type provides a better and interesting position, data and special structure. These methods have differences in the manner of implementation, understandability and speed of response and each is included in a special field of the data classification. Learning process in machine learning is the most important part which causes to elevate the power of a model and can learn the trained problem more quickly and work with it. In this paper, it will present a new method for data classification by Cellular Learning Automata. This method includes three stages. In order to show the power of this model, we have tested it on several types of online dataset and study it in terms of the learning speed, accuracy and simplicity i...

2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS), 2012

The Cellular Neural/Nonlinear Network (CNN) paradigm has recently led to a Bio-inspired (Bi-i) Cellular Vision system, which represents a computing platform consisting of sensing, array sensing-processing and digital signal processing. This paper illustrates the implementation of a novel CNN-based segmentation algorithm onto the Bi-i system. The experimental results, carried out for a benchmark video sequence, show the feasibility of the approach, which provides a frame rate of about 26 frame/sec. Finally, comparisons with existing CNN-based methods highlight that the proposed implementation represents a good trade-off between real-time requirements and accuracy.