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A proposed fuzzy neural model based on mutual subsethood measure / Nelly Saeed Mohammed Amer ; Supervised Hesham Ahmed Hefny

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Nelly Saeed Mohammed Amer , 2015Description: 110 Leaves : charts ; 30cmOther title:
  • مقترح لنموذج فازى شبكى عصبى قائم على التشابه المعتمد على الإحتواء الفئوى المتبادل [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Sciences Summary: This thesis presents a proposed fuzzy neural model based on mutual subsethood measure that handles both of numeric and linguistic inputs simultaneously. Fuzzy rule- based knowledge is interpreted into network architecture. Connections in the network are represented by Bell fuzzy sets. The firing degrees of the fuzzy IF-Then rules in the proposed model are obtained based on fuzzy mutual subsethood similarity measure, which is computed neither approximately nor numerically. It is computed by an exact formula. A supervised learning procedure based on gradient descent is employed to train the network. The proposed model is considered to be a fuzzy neural model with high nonlinear capabilities. The proposed model is featured by its low cost of computations since it utilizes only one generalized analytical formula for computing firing degree of the fuzzy If-Then rule assigned to each fuzzy neuron. Conversely, other fuzzy neural models suffer of high cost of computations since they utilize several analytical formulas for different cases that results in long and tedious calculation and time consuming
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.02.M.Sc.2015.Ne.P (Browse shelf(Opens below)) Not for loan 01010110068825000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.02.M.Sc.2015.Ne.P (Browse shelf(Opens below)) 68825.CD Not for loan 01020110068825000

Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Sciences

This thesis presents a proposed fuzzy neural model based on mutual subsethood measure that handles both of numeric and linguistic inputs simultaneously. Fuzzy rule- based knowledge is interpreted into network architecture. Connections in the network are represented by Bell fuzzy sets. The firing degrees of the fuzzy IF-Then rules in the proposed model are obtained based on fuzzy mutual subsethood similarity measure, which is computed neither approximately nor numerically. It is computed by an exact formula. A supervised learning procedure based on gradient descent is employed to train the network. The proposed model is considered to be a fuzzy neural model with high nonlinear capabilities. The proposed model is featured by its low cost of computations since it utilizes only one generalized analytical formula for computing firing degree of the fuzzy If-Then rule assigned to each fuzzy neuron. Conversely, other fuzzy neural models suffer of high cost of computations since they utilize several analytical formulas for different cases that results in long and tedious calculation and time consuming

Issued also as CD

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