Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Hierarchies of Self-Organizing Maps for action recognition
2016
We propose a hierarchical neural architecture able to recognise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It represents the dynamics of the stream of posture frames in action sequences as activity trajectories over time. The second layer in the hierarchy consists of another SOM which clusters the activity trajectories of the first-layer SOM and learns to represent action prototypes. The third - and last - layer of the hierarchy consists of a neural network that learns to label action prototypes of the second-laye…
Analysis of motor control and behavior in multi agent systems by means of artificial neural networks
2004
Abstract This article gives a short introduction to Self-Organizing Maps, a particular form of Artificial Neural Networks and shows by some examples, how these approaches can be used in order to analyze and visualize time series data originating from complex systems. The methods shown in this article have originally been developed for the analysis of RoboCup robot soccer games, a special kind of so-called Multi Agent Systems. Although this application has no direct connection to biomechanics, the examples shown here may give an impression of the abilities of Neural Networks in the field of Time Series Analysis in general. Because of the abstractness of the methods, it appears to be very lik…
Self-organizing maps: A new digital architecture
1991
An original hardware architecture implementing the self-organizing feature maps, which is one of the most powerful and efficent neural network algorithm, is presented. The architecture, contrary to the most investigated hardware implementations of neural networks, is a full digital one and it may be easily built by using the standard VLSI techniques.
The Hydrothermal System of Solfatara Crater (Campi Flegrei, Italy) Inferred From Machine Learning Algorithms
2019
Two machine learning algorithms were applied to three multivariate datasets acquired at Solfatara volcano. Our aim was to find an unbiased and coherent synthesis among the large amount of data acquired within the crater and along two orthogonal vertical NNE- and WNW-trending cross-sections. The first algorithm includes a new approach for a soft K-means clustering based on the use of the silhouette index to control the color palette of the clusters. The second algorithm which uses the self-organizing maps incorporates an alternative method for choosing the number of nodes of the neural network which aims to avoid the need for downstream clustering of the results of the classification. Both m…
Forecasting daily urban electric load profiles using artificial neural networks
2004
The paper illustrates a combined approach based on unsupervised and supervised neural networks for the electric energy demand forecasting of a suburban area with a prediction time of 24 h. A preventive classification of the historical load data is performed during the unsupervised stage by means of a Kohonen's self organizing map (SOM). The actual forecast is obtained using a two layered feed forward neural network, trained with the back propagation with momentum learning algorithm. In order to investigate the influence of climate variability on the electricity consumption, the neural network is trained using weather data (temperature, relative humidity, global solar radiation) along with h…
A neural network approach to movement pattern analysis.
2004
Movements are time-dependent processes and so can be modelled by time-series of coordinates: E.g., each articulation has geometric coordinates; the set of the coordinates of the relevant articulations build a high-dimensional configuration. These configurations--or "patterns"--give reason for analysing movements by means of neural networks: The Kohonen Feature Map (KFM) is a special type of neural network, which (after having been coined by training with appropriate pattern samples) is able to recognize single patterns as members of pattern clusters. This way, for example, the particular configurations of a given movement can be identified as belonging to respective configuration clusters, …
Visual Data Mining With Self-organizing Maps for “Self-monitoring” Data Analysis
2016
Data collected in psychological studies are mainly characterized by containing a large number of variables (multidimensional data sets). Analyzing multidimensional data can be a difficult task, especially if only classical approaches are used (hypothesis tests, analyses of variance, linear models, etc.). Regarding multidimensional models, visual techniques play an important role because they can show the relationships among variables in a data set. Parallel coordinates and Chernoff faces are good examples of this. This article presents self-organizing maps (SOM), a multivariate visual data mining technique used to provide global visualizations of all the data. This technique is presented as…
Tree Structured Self-Organizing Maps
1999
Publisher Summary This chapter provides an overview of the tree structured self-organizing maps (TS-SOM). It was originally intended as a fast implementation of the self-organizing map (SOM). The chapter explains that TS-SOM is a constructive smoother for a class of dimension reduction problems. There is a well known relation between self-organizing maps and principal curves. Unfortunately in most presentations it is derived by simple reasoning, avoiding the mathematical statement of the problem, which is essential to understand how efficient SOM implementations can be constructed. In this chapter, SOM is derived as a numerical solution of a generic model in a continuous domain, which diffe…
A Study of the Simulated Evolution of the Spectral Sensitivity of Visual Agent Receptors
2001
In this article we study a model for the evolution of the spectral sensitivity of visual receptors for agents in a continuous virtual environment. The model uses a genetic algorithm (GA) to evolve the agent sensors along with the control of the agents by requiring the agents to solve certain tasks in the simulation environment. The properties of the evolved sensors are analyzed for different scenarios. In particular, it is shown that the GA is able to find a balance between sensor costs and agent performance in such a way that the spectral sensor sensitivity reflects the emission spectrum of the target objects and that the capability of the sensors to evolve can help the agents significantl…
Sabiedrības attieksmes modelēšana, izmantojot sentimenta analīzi
2017
Šī darba mērķis ir izveidot sentimenta analīzes risinājumu, kuru paredzēts izmantot informācijas ieguves sistēmas koncepta izstrādē. Sentimenta analīze tiks veikta sociālo tīklu ziņām. Darba izstrādes sākumā tika veikta esošo sentimenta analīzes risinājumu izpēte un to rezultātu salīdzināšana. Tālāk tika veikta publiski pieejamo treniņdatu korpusu ievākšana. Papildus iegūtajiem datiem, tika izveidots latviešu valodai paredzēts sentimenta analīzes treniņdatu korpuss. Korpusa izveidošanas procesā tika veikta informācijas ieguves sistēmas koncepta izveide. Pēc nepieciešamo treniņdatu savākšanas, tika veikta ilgās īstermiņa atmiņas rekurentā neirona tīkla izveidošana un optimizēšana. Darba rezu…