In summary , the results of this paper will improve coal adsorption theory and testing method , and provide the theoretical basis for researching cbm storing mechanism and technical support for cbm resource evaluation and economical recovery 通過本論文的研究,進(jìn)一步豐富了煤吸附理論,完善了測(cè)試方法,為煤層氣的儲(chǔ)集機(jī)理的研究提供了理論基礎(chǔ),為煤層氣資源評(píng)價(jià)和經(jīng)濟(jì)開采提供了技術(shù)支持。
In this paper , the artificial neural networks are considered as a structure set of the neurons . based on this point of view , we make a comprehensive and deep researching on the hopfield model neural network of associative memory with hebbian learning in three aspects , i . e . , analyzing , describing and computing of the symmetry of the system , thus discovering the storing mechanism of the hebbian learning rule . which give a deeper understanding to the associative memory mechanism of artificial neural network 本文將人工神經(jīng)網(wǎng)絡(luò)視為神經(jīng)元的結(jié)構(gòu)集,并從這個(gè)基本觀點(diǎn)出發(fā),從三個(gè)方面,即對(duì)稱性的分析、表示以及計(jì)算,對(duì)hebb型的離散hopfield模型神經(jīng)網(wǎng)絡(luò)進(jìn)行全面的、深入的研究,揭示了hebb法則這種特殊的存儲(chǔ)規(guī)則的機(jī)理,并以此來達(dá)到加深對(duì)整個(gè)網(wǎng)絡(luò)的聯(lián)想記憶機(jī)理認(rèn)識(shí)的目的。