By Elmar Berghöfer, Denis Schulze, Marko Tscherepanow, Sven Wachsmuth (auth.), Lazaros Iliadis, Chrisina Jayne (eds.)
The two-volume set IFIP AICT 363 and 364 constitutes the refereed court cases of the twelfth overseas convention on Engineering purposes of Neural Networks, EANN 2011, and the seventh IFIP WG 12.5 foreign convention, AIAI 2011, held together in Corfu, Greece, in September 2011. The fifty two revised complete papers and 28 revised brief papers awarded including 31 workshop papers have been rigorously reviewed and chosen from one hundred fifty submissions. the 1st quantity contains the papers that have been accredited for presentation on the EANN 2011 convention. they're geared up in topical sections on laptop imaginative and prescient and robotics, self organizing maps, classification/pattern attractiveness, monetary and administration functions of AI, fuzzy platforms, help vector machines, studying and novel algorithms, reinforcement and radial foundation functionality ANN, laptop studying, evolutionary genetic algorithms optimization, net purposes of ANN, spiking ANN, function extraction minimization, scientific functions of AI, environmental and earth applicatons of AI, multi layer ANN, and bioinformatics. the quantity additionally includes the authorized papers from the Workshop on Appliations of sentimental Computing to Telecommunication (ASCOTE 2011), the Workshop on Computational Intelligence purposes in Bioinformatics (CIAB 2011), and the second one Workshop on Informatics and clever structures purposes for caliber of lifestyles details providers (ISQLIS 2011).
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Additional resources for Engineering Applications of Neural Networks: 12th INNS EANN-SIG International Conference, EANN 2011 and 7th IFIP WG 12.5 International Conference, AIAI 2011, Corfu, Greece, September 15-18, 2011, Proceedings Part I
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1 Hierarchical Clustering Hierarchical clustering results based on Euclidean distance and several linkage methods are shown in Figure 1. 1937. 4698, is obtained by applying the Ward distance. 2 K-means Due to the sensitivity of the k-means algorithm to become trapped in a local minimum, the algorithm was restarted 100 times from various random initial positions. This effectively converged into a unique solution, as presented in Figure 2. 4742, which slightly exceeds the result obtained by hierarchical clustering.
An application of clustering methods for production planning is proposed. Hierarchical clustering, k-means and SOM clustering are applied to production data from the company KGL in Slovenia. A database of 252 products manufactured in the company is clustered according to the required operations and product features. Clustering results are evaluated with an average silhouette width for a total data set and the best result is obtained by SOM clustering. In order to make clustering results applicable to industrial production planning, a percentile measure for the interpretation of SOM clusters into the production cells is proposed.
This ANN aims to learn the driver’s behavior, providing smoother steering and levels of speed in the same way as the driver. We analyze six levels of template memory on the ANN searching to obtain the topology which provides the more reliable ANN. Also, we analyze many supervised ML algorithms to compare with this ANN in order to find the best among them. This paper is organized as follows. Section 2 presents the related works. Section 3 describes the proposed method. Section 4 shows the experimental results and discussion.