By Jian Fu, Haibo He, Qing Liu, Zhen Ni (auth.), Derong Liu, Huaguang Zhang, Marios Polycarpou, Cesare Alippi, Haibo He (eds.)
The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed court cases of the eighth overseas Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011.
The overall of 215 papers offered in all 3 volumes have been rigorously reviewed and chosen from 651 submissions. The contributions are established in topical sections on computational neuroscience and cognitive technological know-how; neurodynamics and complicated structures; balance and convergence research; neural community types; supervised studying and unsupervised studying; kernel equipment and help vector machines; blend versions and clustering; visible conception and development popularity; movement, monitoring and item reputation; average scene research and speech acceptance; neuromorphic undefined, fuzzy neural networks and robotics; multi-agent platforms and adaptive dynamic programming; reinforcement studying and determination making; motion and motor keep an eye on; adaptive and hybrid clever platforms; neuroinformatics and bioinformatics; info retrieval; info mining and information discovery; and common language processing.
Read Online or Download Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part III PDF
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Additional info for Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part III
Once the qualities are categorized, the product can be designed to meet the different requirements of each quality attribute, according to its category. Comparing the performance of the current design with that of the average design can clarify the strengths and weaknesses of the current design. Referring to the definition of the Kano categories, the priority for attribute improvement can be determined. Figure 5 depicts the decision matrix. According to the decision matrix, one-dimensional qualities, which can create user satisfaction and have an actual competitive advantage, should be improved in all situations.
5 shows the success rates of the original DHP and the improved DHP. The success rate denotes the ratio of the number of successful learning processes to the total number of learning episodes, which is 500 in the simulation. According to the results, a distinct improvement is achieved for the improved DHP adopting the Delta-Bar-Delta rule. The above experimental results show that the improved DHP gains advantages over the original one. The Delta-Bar-Delta rule helps the networks learn with adaptive learning rates, and then achieve better convergence performance and success rates.
Then 32 male and 28 female subjects between 18-24 years old were recruited for the SD evaluation experiment. All 60 subjects were asked to evaluate the 35 experimental samples of mobile phones, with a 7-point Likert scale (-3-+3) of the SD method, on each of the 20 affective qualities and overall satisfaction. 2, was used to classify these qualities into different Kano categories. The means of 20 qualities for each phone design were then transformed into negative and positive performance, respectively.
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