By Chin-Pao Hung, Mang-Hui Wang (auth.), Jose Manuel Benítez, Oscar Cordón, Frank Hoffmann, Rajkumar Roy (eds.)
Soft computing embraces methodologies for the advance of clever structures which were effectively utilized to numerous real-word difficulties. This selection of keynote papers, provided on the seventh online global convention on delicate Computing in Engineering layout and production, presents a finished evaluate of contemporary advances in fuzzy, neural and evolutionary computing innovations and purposes in engineering layout and production.
- New and hugely complex examine effects on the leading edge of soppy computing in engineering layout and production.
- Keynote papers by means of world-renowned researchers within the box.
- a great assessment of present delicate computing examine world wide.
A number of methodologies aimed toward researchers layout and production engineers who increase and observe clever platforms in desktop engineering.
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Extra info for Advances in Soft Computing: Engineering Design and Manufacturing
Department of Architecture and Computer Technology University of Granada. Campus de Fuentenueva. E. 18071 Granada (Spain) 2 Department of Technology IES Europa. C Miguel Angel Blanco sin. Aguilas, Murcia (Spain) e-mail: pedrolOatc . ugr . es URL: http: / / geneura. ugr . es Summary. Although a great amount of algorithms have been devised to train the weights of a neural network for a fixed topology, most of them are hillclimbing procedures, which usually fall in a local optimum; that is why results obtained depend to a great extent on the learning parameters and the initial weights as well as on the network topology.
Temp. 979 6 Conclusions In this paper, we compared the performance of MLP network, ERNN and RBFN. Compared to the MLP neural network, the ERNN could efficiently capture the dynamic behavior of the weather, resulting in a more compact and natural internal representation ofthe temporal information contained in the weather profile. ERNN took more training time but it is dependent on the training data size and the number of network parameters. It can be inferred that ERNN could yield more accurate results, if good data selection strategies, training paradigms, and network input and output representations are determined properly.
All the obtained results were compared and evaluated by the Maximum Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Absolute Deviation (MAD). Test results (August 2001) for the actual versus predicted minimum/maximum temperature and wind speed using MLP 26 Imran Maqsood, Muhammad Riaz Khan, and Ajith Abraham and ERNN with OSS and LM approaches are plotted in Figures 4, 5 and 6 respectively. Empirical results are depicted in Tables 2 through 4. -~~~~-~-~~--.. ' ........