The diploma thesis focuses on classification of emotions. Thesis deals about parameterization of sounds files by suprasegment and segment methods with regard for next used of these methods. Berlin database is used. This database includes many of sounds records with emotions. Parameterization creates files, which are divided to two parts. First part is used for training and second part is used for testing. Point of interest is self-organization network. Thesis includes Matlab´s program which can be used for parameterization of any database. Data are classified by self-organization network after parameterization. Results of hits rates are presented at the end of this diploma thesis.

The project deals with artificial neural networks. After designing and debugging the test data set and the training sample set, we created a multilayer perceptron network in the Neural NetworkToolbox (NNT) of Matlab. When creating networks, we used different training algorithms and algorithms improving the generalization of the network. When creating a radial basis network, we did not use the NNT, but a specific source code in Matlab was written. Functionality of neural networks was tested on simple training and testing patterns. Realistic training data were obtained by the simulation of twelve monoconic antennas operating in the frequency range from 2 to 6 GHz. Antennas were located inside a mathematical model of Octavia II. Using CST simulations, electromagnetic fields in a car were obtained. Trained networks are described by regressive characteristics andthe mean square error of training. Algorithms improving generalization are applied on the created and trained networks. The performance of individual networks is mutually compared.

The diploma thesis is focused on the long term prediction of mean monthly flows in hydrometric profile for purposes of reservoir control optimization and optimization of reservoir systems. Discharges were predicted using by artificial neural network method. Predicted flows were statistically evaluated by relevant coefficients and then compared with the measured flows for given river hydrometric profiles.

The thesis is dedicated to the study and implementation of methods used for learning from the course of playing. The chosen game for this thesis is Backgammon. The algorithm used for training neural networks is called the temporal difference learning with use of eligible traces. This algorithm is also known as TD(lambda). The theoretical part describes algorithms for playing games without learning, introduction to reinforcement learning, temporal difference learning and introduction to artificial neural networks. The practical part deals with application of combination of neural networks and TD(lambda) algorithms.

Main subjects of this thesis are texture classification and texture-based object recognition. Various texture features are being explored, including several variants of local binary patterns (LBP). A novel modification of LBP (weighted spatial LBP) is proposed, with intention to improve on the spatial coverage of the traditional LBP. Rarely used color texture features are being discussed as well. Artificial neural networks and support vector machines are used to classify all the aforementioned features. Using these methods, framework for the texture classification and image segmentation is implemented. Comprehensive texture database is employed to test its performance under different conditions. In the end, the system is applied to solve a real-world problem - the segmentation of aerial photos.

This doctoral thesis deals with possibilities of using advanced methods of decision-making - Soft Computing, in company’s ordering cycle management. The main aim of the thesis is to propose an artificial neural network model with an optimal architecture for ordering cycle management within the supply chain management. The proposed model will be employed in an organization involved in retailing to ensure smooth material flow. A design and verification of artificial neural networks model for sales prediction is also part of this doctoral thesis as well as a comparison of results and usability with standard and commonly used statistical methods. Furthermore, the thesis deals with finding a suitable artificial neural network model with architecture capable of solving the lot-size problem according to specified inputs. Methods of statistical data processing, economical modelling and advanced decision-making (Soft Computing) were utilized during the model designing process.

The aim of this work is the genetic design of neural networks, which are able to classify within various classification tasks. In order to create these neural networks, algorithm called NeuroEvolution of Augmenting Topologies (also known as NEAT) is used. Also the idea of preprocessing, which is included in implemented result, is proposed. The goal of preprocessing is to reduce the computational requirements for processing of benchmark datasets for classification accuracy. The result of this work is a set of experiments conducted over a data set for cancer cells detection and a database of handwritten digits MNIST. Classifiers generated for the cancer cells exhibits over 99 % accuracy and in experiment MNIST reduces computational requirements more than 10 % with bringing negligible error of size 0.17 %.

The thesis is focused on analysis of polysomnographic signals based on extraction of chosen parameters in time, frequency and time-frequency domain. The parameters are acquired from 30 seconds long segments of EEG, EMG and EOG signals recorded during different sleep stages. The parameters used for automatic classification of sleep stages are selected according to statistical analysis. The classification is realized by artificial neural networks, k-NN classifier and linear discriminant analysis. The program with a graphical user interface was created using Matlab.

L’Intelligence Ambiamte (AmI) est un domaine de recherche investigant les techniques d’intelligence artificielle pour créer des environnements réactifs. Les réseaux de capteurs et effecteurs sans-fils sont les supports de communication entre les appareils ménagers, les services installés et les interfaces homme-machine. Cette thèse s’intéresse à la conception d’Environements Réactifs avec des propriétés autonomiques i.e. des systèmes qui ont la capacité de se gérer eux-même. De tels environements sont ouverts, à grande échelle, dynamique et hétérogène, ce qui induit certains problèmes pour leur gestion par des systèmes monolithiques. L’approche proposée est bio-inspirée en considérant chacune des plate-formes comme une cellule indépendente formant un organisme intelligent distribué. Chaque cellule est programmée par un processus ADN-RNA décrit par des règles réactives décrivant leur comportement interne et externe. Ces règles sont modelées par des agents mobiles ayant des capacités d’auto-réécriture et offrant ainsi des possibilités de reprogrammation dynamique. Le framework EMMA est composé d’un middleware modulaire avec une architecture orientée ressource basée sur la technologie 6LoWPAN et d’une architecture MAPE-K pour concevoir des AmI à plusieurs échelles. Les différentes relations entre les problèmes techniques et les besoins théoriques sont discutées dans cette thèse depuis les plate-formes, le réseau, le middleware, les agents mobiles, le déploiement des applications jusqu’au système intelligent. Deux algorithmes pour AmI sont proposés : un modèle de contrôleur neuronal artificiel pour le contrôle automatique des appareils ménagers avec des processus d’apprentissage ainsi qu’une procédure de vote distribuée pour synchroniser les décisions de plusieurs composants systèmes. / AThe Ambient Intelligence (AmI) is a research area investigating AI techniques to create Responsive Environments (RE). Wireless Sensor and Actor Network (WSAN) are the supports for communications between the appliances, the deployed services and Human Computer Interface (HCI). This thesis focuses on the design of RE with autonomic properties i.e. system that have the ability to manage themselves. Such environments are open, large scale, dynamic and heterogeneous which induce some difficulties in their management by monolithic system. The bio-inspired proposal considers all devices like independent cells forming an intelligent distributed organism. Each cell is programmed by a DNA-RNA process composed of reactive rules describing its internal and external behaviour. These rules are modelled by reactive agents with self-rewriting features offering dynamic reprogramming abilities. The EMMA framework is composed of a modular Resource Oriented Architecture (ROA) Middleware based on IPv6 LoW Power Wireless Area Networks (6LoWPAN) technology and a MAPE-K architecture to design multi-scale AmI. The different relations between technical issues and theoretical requirements are discussed through the platforms, the network, the middleware, the mobile agents, the application deployment to the intelligent system. Two algorithms for AmI are proposed: an Artificial Neural Controller (ANC) model for automatic control of appliances with learning processes and a distributed Voting Procedures (VP) to synchronize the decisions of several system components over the WSAN.

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