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Recently, a need has arisen for prediction techniques that can address a variety of problems by combining methods from the rapidly developing field of machine learning with geoinformation technologies such as GIS, remote sensing, and GPS. As a result, over the last few decades, one particular machine learning technology, known as artificial neural networks, has been successfully applied to a wide range of fields in science and engineering. In addition, the development of computational and spatial technologies has led to the rapid growth of geoinformatics, which specializes in the analysis of spatial information. Thus, recently, artificial neural networks have been applied to geoinformatics and have produced valuable results in the fields of geoscience, environment, natural hazards, natural resources, and engineering. Hence, this Special Issue of the journal Applied Sciences, "Application of Artificial Neural Networks in Geoinformatics," was successfully planned, and we here publish a collection of papers detailing novel contributions that are of relevance to these topics.

Neural networks (Computer science) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing

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In this book, highly qualified multidisciplinary scientists grasp their recent researches motivated by the importance of artificial neural networks. It addresses advanced applications and innovative case studies for the next-generation optical networks based on modulation recognition using artificial neural networks, hardware ANN for gait generation of multi-legged robots, production of high-resolution soil property ANN maps, ANN and dynamic factor models to combine forecasts, ANN parameter recognition of engineering constants in Civil Engineering, ANN electricity consumption and generation forecasting, ANN for advanced process control, ANN breast cancer detection, ANN applications in biofuels, ANN modeling for manufacturing process optimization, spectral interference correction using a large-size spectrometer and ANN-based deep learning, solar radiation ANN prediction using NARX model, and ANN data assimilation for an atmospheric general circulation model.

Neural networks (Computer science) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Physical Sciences --- Engineering and Technology --- Neural Network --- Computer and Information Science --- Artificial Intelligence

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"Somewhere, there is always wind blowing or the sun shining." This maxim could lead the global shift from fossil to renewable energy sources, suggesting that there is enough energy available to be turned into electricity. But the already impressive numbers that are available today, along with the European Union's 20-20-20 goal - to power 20% of the EU energy consumption from renewables until 2020 -, might mislead us over the problem that the go-to renewables readily available rely on a primary energy source mankind cannot control: the weather. At the same time, the notion of the smart grid introduces a vast array of new data coming from sensors in the power grid, at wind farms, power plants, transformers, and consumers. The new wealth of information might seem overwhelming, but can help to manage the different actors in the power grid. This book proposes to view the problem of power generation and distribution in the face of increased volatility as a problem of information distribution and processing. It enhances the power grid by turning its nodes into agents that forecast their local power balance from historical data, using artificial neural networks and the multi-part evolutionary training algorithm described in this book. They pro-actively communicate power demand and supply, adhering to a set of behavioral rules this book defines, and finally solve the 0-1 knapsack problem of choosing offers in such a way that not only solves the disequilibrium, but also minimizes line loss, by elegant modeling in the Boolean domain. The book shows that the Divide-et-Impera approach of a distributed grid control can lead to an efficient, reliable integration of volatile renewable energy sources into the power grid.

Neural networks (Computer science) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Smart Grid --- Power Grid Management --- Artificial Intelligence --- Boolean Algebra

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The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models. This book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.

Neural networks (Computer science) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Physical Sciences --- Engineering and Technology --- Computer and Information Science --- Machine Learning --- Numerical Analysis and Scientific Computing

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"Mendel contains peer-reviewed experimental and theoretical papers on evolutionary computation, genetic programming, swarm Intelligence, neural networks, fuzzy logic, big data, Bayesian methods, intelligent image processing, bio-inspired robotics."

evolutionary computation --- soft computing --- neural networks --- fuzzy logic --- optimization --- bio-inspired robotics --- Computational intelligence --- Soft computing --- Neural networks (Computer science) --- Soft computing. --- Computational intelligence. --- Cognitive computing --- Electronic data processing --- Intelligence, Computational --- Artificial intelligence --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Natural computation