A Neural Network Model for Time-Series Forecasting

The paper presents some aspects regarding the use of pattern recognition techniques and neural networks for the activity evolution diagnostication and prediction by means of a set of indicators. Starting from the indicators set there is defined a measure on the patterns set, measure representing a scalar value that characterizes the activity analyzed at each time moment. A pattern is defined by the values of the indicators set at a given time. Over the classes set obtained by means of the classification and recognition techniques is defined a relation that allows the representation of the evolution from negative evolution towards positive evolution. For the diagnostication and prediction the following tools are used: pattern recognition and multilayer perceptron. The paper also presents the REFORME software written by the authors and the results of the experiment obtained with this software for macroeconomic diagnostication and prediction during the years 2003-2010.time-series, pattern recognition, neural networks, multilayer perceptron, diagnostication, forecasting

Partially Supervised Approach in Signal Recognition

The paper focuses on the potential of principal directions based approaches in signal classification and recognition. In probabilistic models, the classes are represented in terms of multivariate density functions, and an object coming from a certain class is modeled as a random vector whose repartition has the density function corresponding to this class. In cases when there is no statistical information concerning the set of density functions corresponding to the classes involved in the recognition process, usually estimates based on the information extracted from available data are used instead. In the proposed methodology, the characteristics of a class are given by a set of eigen vectors of the sample covariance matrix. The overall dissimilarity of an object X with a given class C is computed as the disturbance of the structure of C, when X is allotted to C. A series of tests concerning the behavior of the proposed recognition algorithm are reported in the final section of the paper.signal processing, classification, pattern recognition, compression/decompression

Partially Supervised Approach in Signal Recognition

The paper focuses on the potential of principal directions based approaches in signal classification and recognition. In probabilistic models, the classes are represented in terms of multivariate density functions, and an object coming from a certain class is modeled as a random vector whose repartition has the density function corresponding to this class. In cases when there is no statistical information concerning the set of density functions corresponding to the classes involved in the recognition process, usually estimates based on the information extracted from available data are used instead. In the proposed methodology, the characteristics of a class are given by a set of eigen vectors of the sample covariance matrix. The overall dissimilarity of an object X with a given class C is computed as the disturbance of the structure of C, when X is allotted to C. A series of tests concerning the behavior of the proposed recognition algorithm are reported in the final section of the paper

PERANCANGAN PERANGKAT LUNAK PENGENALAN POLA KARAKTER MENGGUNAKAN JARINGAN SYARAF TIRUAN PERCEPTRON

Abstracts: Artificial Neural Net is a method in soft computing that imitate the structure of biological nervous, where multiple nodes communicate with each other through synapses that interconnect them. That method can be utilized for pattern recognition processes, such as hand writing character recognition. This paper is aimed to develop hand writing character recognition using Artificial Neural Net method. Perceptron neural network is adopted in Artificial Neural Net. A mapping method is used for preprocessing to segment a character image to be processed by Artificial Neural Net. The experimental results show that among all of successful segmented characters of all the training data. the system recognizes the characters with an accuracy close to 79,2%.Keywords: Character Recognition, Artificial Neural Net, Image Processing,Perceptro

PRIMJENA METODOLOGIJA MEKOGA RAČUNARSTVA U PREDVIĐANJU 28-DNEVNE TLAČNE ČVRSTOĆE MLAZNOGA BETONA: KOMPARATIVNA USPOREDBA INDIVIDUALNOGA I HIBRIDNOGA MODELA

Shotcreting is a popular construction technique with wide-ranging applications in mining and civil engineering. Compressive strength is a primary mechanical property of shotcrete with particular importance for project safety, which highly depends on its mix design. But in practice, there is no reliable and accurate method to predict this strength. In this study, existing experimental data related to shotcretes with 59 different mix designs are used to develop a series of soft computing methodologies, including individual artificial neural network, support vector regression, and M5P model tree and their hybrids with the fuzzy c-means clustering algorithm so as to predict the 28-day compressive strength of shotcrete. Analysis of the results shows the superiority of the hybrid model over the individual models in predicting the compressive strength of shotcrete. Overall, data clustering prior to use of machine learning techniques leads to certain improvement in their performance and reliability and generalizability of their results. In particular, the M5P model tree exhibits excellent capability in anticipating the compressive strength of shotcrete.Mlazni beton popularna je konstrukcijska tehnika široke uporabe u rudarstvu i građevinarstvu. Tlačna čvrstoća primarno je mehaničko svojstvo mlaznoga betona s posebnom važnošću za sigurnost projekta, ovisno o sastavu betona. U praksi ne postoji pouzdana i točna metoda za predviđanje toga svojstva. Ovdje su prikazani eksperimentalni podatci za 59 različitih sastava mlaznoga betona, na kojima je razvijen niz metodologija temeljem mekoga računarstva, uključujući pojedinačnu umjetnu neuronsku mrežu, podržanu vektorskom regresijom, stablastim dijagramima, njihovim hibridima na temelju klastera vrijednosti c-sredina, a s ciljem predviđanja promjene tlačne čvrstoće mlaznoga betona tijekom 28 dana. Općenito su klasteri podataka već prije uporabe strojnoga učenja znatno pomogli u kvaliteti, pouzdanosti i općenitosti rezultata. Posebno je istaknut stablasti model M5P kao onaj koji izvrsno predviđa tlačnu čvrstoću mlaznoga betona

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,