Efficient Bayesian inference for harmonic models via adaptive posterior factorization

NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in NEUROCOMPUTING, [VOL72, ISSUE 1-3, (2008)] DOI10.1016/j.neucom.2007.12.05

Reply to determining structural identifiability of parameter learning machines

The paper Ran and Hu (2014, Neurocomputing) examines identifiability and parameter redundancy in classes of models used in machine learning. This note discusses the results on global identifiability and also clarifies that the paper's results on parameter redundancy already exist in the paper Cole et al. (2010, Mathematical Biosciences)

ADAPTATION OF NEUROCOMPUTING INTERFACE FOR CONTROLLING THE STUDENT'S KNOWLEDGE

Advances in development of biosensor technology in the new century allowed to start using neurocomputing interfaces and electroencephalogram (EEG) of the users to analyze human activities. Existing studies show that neurocomputing interfaces enable an objective assessment of the state of the user. In this paper we attempt to adapt neurocomputing interfaces for analysis of students' knowledge as users of information systemДостижения в области развития технологий биосенсоров в новом веке позволили приступить к использованию нейрокомпьютерных интерфейсов и электроэнцефалограмм (ЭЭГ) пользователей в анализе деятельности человека. Существующие исследования показывают, что нейрокомпьютерные интерфейсы позволяют дать объективную оценку состояния пользователя. В данной работе нейрокомпьютерные интерфейсы адаптируются для анализа знаний студентов, которые взаимодействуют с информационной системо

An adaptive stereo basis method for convolutive blind audio source separation

NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PUBLICATION, [71, 10-12, June 2008] DOI:neucom.2007.08.02

Implementation of Microbe-Based Neurocomputing with Euglena Cells Confined in Micro-Aquariums

Using real Euglena cells in a micro-aquarium as photoreactive biomaterials, we demonstrated Euglena-based neurocomputing with two-dimensional optical feedback using the modified Hopfield–Tank algorithm. The blue light intensity required to evoke the photophobic reactions of Euglena cells was experimentally determined, and the empirically derived autoadjustment of parameters was incorporated in the algorithm. The Euglenabased neurocomputing of 4-city traveling salesman problem possessed two fundamental characteristics: (1) attaining one of the best solutions of the problem and (2) searching for a number of solutions via dynamic transition among the solutions (multi-solution search). The spontaneous reduction in cell number in illuminated areas and the existence of photoinsensitive robust cells are the essential mechanisms responsible for the two characteristics of the Euglena-based neurocomputing

Neurocomputing fundamental climate analysis

Rainfall is a natural phenomenon that needs to be studied more deeply and interesting to be analyzed. It involves numbers of human activities such as aviation, agriculture, fisheries, and also disaster risk reduction. Moreover, the characteristics of rainfall data follows seasonality, fluctuation, not normally distributed and it makes traditional time series challenging to use. Therefore, neurocomputing model can be used as an alternative to extraction information from rainfall data and give high performance also accuracy. In this paper, we give short preview about SST Anomalies in Manado, Northern Sulawesi and at the same time comparing the performance of rainfall forecasting by using three types of neurocomputing methods such as Generalized Regression Neural Network (GRNN), Feed forward Neural Network (FFNN), and Localized Multi Kernel Support Vector Regression (LMKSVR). In a nutshell, all of neurocomputing methods give highly accurate forecasting as well as reach low MAPE FFNN 1.65%, GRNN 2.65% and LMKSVR 0.28%, respectively

Fundamentals in Neurocomputing

Neurocomputing - inspired from neuroscience - provides the potential of an alternative information processing paradigm that involves large interconnected networks of relatively simple and typically non-linear processing elements, so-called (artificial) neural networks. There has been a recent resurgence in the field of neural networks, caused by new net topologies and algorithms, and the belief that massive parallelism is essential for high peiformance in several research areas, especially in pattern recognition. This contribution provides a brief introduction to some basic features of neural networks by defining a neural network, reflecting current thinking about the processing that should be peiformed at each processing element of a neural network, discussing the general categories of training that are commonly used to adjust a neural network's weight vector, and finally by characterizing the backpropagation neural networ:k which is one of the most important historical developments in neurocomputing.- The contribution concludes with pointing to some hot topics for future research. It is hoped that this contribution will stimulate the study of neural networks in quantitative geography and regional science. (author's abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Neural Learning of Vector Fields for Encoding Stable Dynamical Systems

Lemme A, Reinhart F, Neumann K, Steil JJ. Neural Learning of Vector Fields for Encoding Stable Dynamical Systems. Neurocomputing. 2014;141:3-14