Dynamic Fuzzy Reliability Analysis of Multistate Systems Based on Universal Generating Function

Reliability Analysis of Structural Systems based on Universal Generating Function

As an important tool for performance assessment in multi-state systems, the universal generating function approach is introduced and developed aiming at strength reliability analysis of structural systems. Static reliability models of statically determinate and statically indeterminate systems are built by constructing special generating functions, which are used to describe probability distrib...

designing unmanned aerial vehicle based on neuro-fuzzy systems

در این پایان نامه، کنترل نرو-فازی در پرنده هدایت پذیر از دور (پهپاد) استفاده شده است ابتدا در روش پیشنهادی اول، کنترل کننده نرو-فازی توسط مجموعه اطلاعات یک کنترل کننده pid به صورت off-line آموزش دیده است و در روش دوم یک کنترل کننده نرو-فازی on-line مبتنی بر شناسایی سیستم توسط شبکه عصبی rbf پیشنهاد شده است. سپس کاربرد این کنترل کننده در پهپاد بررسی شده است و مقایسه ای ما بین کنترل کننده های معمو...

2. The Universal Generating Function in Reliability Analysis of Binary Systems

While the most effective applications of the UGF method lie in the field of the MSS reliability, it can also be used for evaluating the reliability of binary systems. The theory of binary systems is well developed. Many algorithms exist for evaluating the reliability of different types of binary system. However, no universal systematic approach has been suggested for the wide range of system ty...

Reliability Analysis for the Multistate Systems

Generating function approach to reliability analysis of structural systems

The generating function approach is an important tool for performance assessment in multi-state systems. Aiming at strength reliability analysis of structural systems, generating function approach is introduced and developed. Static reliability models of statically determinate, indeterminate systems and fatigue reliability models are built by constructing special generating functions, which are...