This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A self-organizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated to trai...

In this paper, a method for automatically creating circuit schematic diagrams from the topological information contained in network data files has been proposed. This method is based on Self-Organizing Map (SOM) neural network and the basic idea behind the method is to let the network span itself according to a given “shape” of the network grid. The topology of a network is defined by the conne...

Results of neural network learning are always subject to some variability, due to the sensitivity to initial conditions, to convergence to local minima, and, sometimes more dramatically, to sampling variability. This paper presents a set of tools designed to assess the reliability of the results of self-organizing maps (SOM), i.e. to test on a statistical basis the confidence we can have on the...

Graphs as data structures are used in many applications, for example image analysis, scene description, natural language processing. The paper deals with Acyclic Graph Data Structures (AGDS) and with a learning process in a model of a Self-Organizing Map (SOM) that has been modified for processing of AGDS. To the modified SOM Neural Network (SOM NN), there are added contexts and counters which ...

An intrusion detection system (IDS) monitors the IP packets flowing over the network to capture intrusions or anomalies. One of the techniques used for anomaly detection is building statistical models using metrics derived from observation of the user's actions. A neural network model based on self organization is proposed for detecting intrusions. The selforganizing map (SOM) has shown to be s...

Recently, research area in neural network based spatial analysis have been receiving increasing attention in the last few years. There are number of reasons and the strongest appeal of Artificial Neural Network (ANN) is the suitability for machine learning in computational adaptivity. Machine learning in computational neural network consist of adjusting connection weights to improve the perform...

The self-organizing map (SOM), as a kind of unsupervised neural network, has been used for both static data management and dynamic data analysis. To further exploit its search abilities, in this paper we propose an SOM-based algorithm (SOMS) for optimization problems involving both static and dynamic functions. Furthermore, a new SOM weight updating rule is proposed to enhance the learning effi...

The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. One of the SOM neural network’s applications is clustering of animals due their features. In this paper we produce an experiment to ana...

The present study provides a detailed description of somatostatin (SOM) distribution and the colocalization pattern of SOM, neuropeptide Y (NPY) and nitric oxide synthase (NOS) in the dorsal striatum (caudate-putamen complex) of the guinea pig. Within the dorsal striatum, SOM is found in a population of medium-sized aspiny interneurons. We found that 97% of all SOM-IR neurons expressed NPY simu...