Zobrazeno 1 - 10
of 32
pro vyhledávání: '"SHIGEHARU KITO"'
Autor:
Shigeharu Kito, Tadashi Hattori
Publikováno v:
Chemical Engineering Science. 62:5575-5578
Numerical partial differentiation of trained neural network pattern was proposed as a tool for identification of primary factors controlling catalytic activity. The validity of the method was demonstrated by applying it to experimentally established
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f7d0f3fa502f3bcf471879a7d7652df0
https://doi.org/10.1016/j.ces.2007.01.038
https://doi.org/10.1016/j.ces.2007.01.038
Partial differentiation of neural network for the analysis of factors controlling catalytic activity
Autor:
Tadashi Hattori, Shigeharu Kito
Publikováno v:
Applied Catalysis A: General. 327:157-163
In order to examine the possibility for identifying the factors controlling catalytic activity by neural network, the numerical partial differentiation of trained neural network was applied to several examples of experimentally established correlatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10892c50bffc412486b1a4a4bdd96f75
https://doi.org/10.1016/j.apcata.2007.05.006
https://doi.org/10.1016/j.apcata.2007.05.006
Publikováno v:
Journal of Applied Chemistry and Biotechnology. 24:229-238
A correlation of salt effect on vapour-liquid equilibria for nonaqueous binary system is presented. The correlation is applicable to systems in which the salt is dissolved in only one of the volatile components. Assumptions are made to obtain a corre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b85cd5786300de10d444d7140e8529da
https://doi.org/10.1002/jctb.2720240406
https://doi.org/10.1002/jctb.2720240406
Autor:
Shigeharu Kito, Tadashi Hattori
Publikováno v:
Catalysis Today. 111:328-332
An artificial neural network was applied to the analysis of factors controlling catalytic activity by taking, as examples, experimentally established correlations of catalytic activities with primary factors including both monotonous and volcano-type
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd0329e13e1d9a8887ed5273a53605aa
https://doi.org/10.1016/j.cattod.2005.10.044
https://doi.org/10.1016/j.cattod.2005.10.044
Publikováno v:
Catalysis Today. 97:41-47
The neural network was applied to the estimation of catalyst deactivation by taking, as an example, methanol conversion into hydrocarbons over ion-exchanged dealuminated mordenites. In the first series, it was attempted to estimate the deactivation r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1dbc6a12540e47f7d469e5dc1122b3c3
https://doi.org/10.1016/j.cattod.2004.04.052
https://doi.org/10.1016/j.cattod.2004.04.052
Autor:
Shigeharu Kito, Tadashi Hattori
Publikováno v:
ChemInform. 23
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08a3d3a571ed221e841acee4fbd2443f
https://doi.org/10.1002/chin.199210316
https://doi.org/10.1002/chin.199210316
Autor:
Tadashi Hattori, Shigeharu Kito
Publikováno v:
Catalysis Today. 10:213-222
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83f51e03be420903f75beb64e51d7e28
https://doi.org/10.1016/0920-5861(91)80066-i
https://doi.org/10.1016/0920-5861(91)80066-i
Publikováno v:
Analytical Sciences. 7:761-764
Artificial neural network was applied for the estimation of strength of acid sites synergistically generated on binary mixed oxides. The acid strength of a series of mixed oxides with one component fixed could be estimated within a reasonable error.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::659367e3e8ed30b3a3f1dc23fe6830f2
https://doi.org/10.2116/analsci.7.supple_761
https://doi.org/10.2116/analsci.7.supple_761
Publikováno v:
Chemical Engineering Science. 45:2661-2667
INCAP-Muse, a prototype expert system for the design of multi-component catalysts, was developed. Started from the reactants and the products given in advance, INCAP-Muse identifies that best suited target reaction, and then selects the multi-compone
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::279a52983633ed76773bd7db80f32b98
https://doi.org/10.1016/0009-2509(90)80156-9
https://doi.org/10.1016/0009-2509(90)80156-9
Publikováno v:
Industrial & Engineering Chemistry Research. 31:979-981
An artificial neural network is applied to the estimation of strength of acid sites synergistically generated in binary mixed oxides. The acid strength is represented as a function of physical/chemical properties of both constituents oxides. The form
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aabc9a9cc5d26635475fae465d462c52
https://doi.org/10.1021/ie00003a046
https://doi.org/10.1021/ie00003a046