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Ivan Jordanov
Researcher at University of Portsmouth
Publications - 57
Citations - 2627
Ivan Jordanov is an academic researcher from University of Portsmouth. The author has contributed to research in topics: Artificial neural network & Supervised learning. The author has an hindex of 17, co-authored 53 publications receiving 2486 citations. Previous affiliations of Ivan Jordanov include Bournemouth University & Brunel University London.
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Knowledge-Based Intelligent Information and Engineering Systems
TL;DR: Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition, and design principles derived from scientific analyses and design constraints imposed by targeted applications have jointly guided the development of many variants of the basic networks.
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From on-line sketching to 2D and 3D geometry: a system based on fuzzy knowledge
TL;DR: This real time system is designed to infer user's sketching intentions, to segment sketched input and generate corresponding geometric primitives: straight lines, circles; arcs, ellipses, elliptical arcs, and B-spline curves.
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Optimal design of linear and non-linear dynamic vibration absorbers
TL;DR: In this paper, an efficient numerical method is applied to obtain optimal parameters for both linear and non-linear damped dynamic vibration absorbers, and the minimization of the vibration response has been carried out for damped as well as undamped force excited primary systems with linear and nonsmooth spring characteristics.
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Feature selection for surface defect classification of extruded aluminum profiles
TL;DR: Every step of the image processing of a novel technique based on gradient-only co-occurrence matrices (GOCM) to detect and classify three distinct classes of surface defects in extruded aluminium profiles is described.
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Radar Emitter Signals Recognition and Classification with Feedforward Networks
TL;DR: A possible application of neural networks for timely and reliable recognition of radar signal emitters is investigated and several neural network topologies, training parameters, input and output coding and machine learning facilitating data transformations are investigated.