Adaptive Design Methods for Checking Sequences

نویسندگان

  • bY Raymond
  • T. Boute
  • Raymond T. Boute
چکیده

The length of checking sequences for sequential machines can be considerably reduced if, instead of preset distinguishing sequences, one uses so-called "distinguishing sets" of sequences, which serve the same purpose but are generally shorter. The design of such a set turns out to be equivalent to the design of an adaptive distinguishing experiment, * though a checking sequence, using a distinguishing set, remains essentially preset. This property also explains the title. All machines having preset distinguishing sequences also have distinguishing sets. In case no preset distinguishing sequences exist, most of the earlier methods call for the use of locating sequences, which result in long checking experiments. However, in many of these cases, a distinguishing set can be found, thus resulting in even more savings in length. Finally, the characterizing sequences used in locating sequences can also be adaptively designed, and thus the basic idea presented below is advantageous even when no distinguishing sets exist. * BY "experiment" we mean the application of sequence(s) to the machine while observing the output. In some instances, the words "experiment" and " sequence" can be used interchangeably.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Coastal Water Level Prediction Model Using Adaptive Neuro-fuzzy Inference System

This paper employs Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict water level that leads to flood in coastal areas. ANFIS combines the verbal power of fuzzy logic and numerical power of neural network for its action. Meteorological and astronomical data of Santa Monica, a coastal area in California, U. S. A., were obtained. A portion of the data was used to train the ANFIS network, wh...

متن کامل

Nusselt Number Estimation along a Wavy Wall in an Inclined Lid-driven Cavity using Adaptive Neuro-Fuzzy Inference System (ANFIS)

In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to determine the Nusselt number (Nu) along a wavy wall in a lid-driven cavity under mixed convection regime. Firstly, the main data set of input/output vectors for training, checking and testing of the ANFIS was prepared based on the numerical results of the lattice Boltzmann method (LBM). Then, the ANFIS was develope...

متن کامل

Adaptive Spectral Separation Two Layer Coding with Error Concealment for Cell Loss Resilience

This paper addresses the issue of cell loss and its consequent effect on video quality in a packet video system, and examines possible compensative measures. In the system's enconder, adaptive spectral separation is used to develop a two-layer coding scheme comprising a high priority layer to carry essential video data and a low priority layer with data to enhance the video image. A two-step er...

متن کامل

Adaptive Neural Fuzzy Inference System Models for Predicting the Shear Strength of Reinforced Concrete Deep Beams

A reinforced concrete member in which the total span or shear span is especially small in relation to its depth is called a deep beam. In this study, a new approach based on the Adaptive Neural Fuzzy Inference System (ANFIS) is used to predict the shear strength of reinforced concrete (RC) deep beams. A constitutive relationship was obtained correlating the ultimate load with seven mechanical a...

متن کامل

Decompositional Design of Testable FSM Networks

It has been well-known that Finite State Machines (FSMs) are difficult to test. The problem of FSM testing can be regarded as an identification problem: to design input sequence (test) capable to distinguish a given (fault-free) FSM from all other (faulty) ones. Testing methods based on checking experiments theory [1,2] are not widely used in practical, because of the high upper bound of checki...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1972