Web2024 Doctorate Batch at Indian Institute of Technology, Guwahati. 2011-2012 Project scholar at NIT Rourkela. Project Title: Fuzzified and … WebDec 23, 2016 · The neural network is first trained here for real fuzzy earthquake data. The trained FNN architecture is then used to simulate earthquakes by feeding various intensities and it is found that the predicted responses given by …
Neural network applications in medicine - ScienceDirect
WebThis study discusses how to fuzzify a feedforward neural network (FNN) to generate a fuzzy forecast that contains the actual value, while minimizing the average range of fuzzy forecasts. This topic has rarely been investigated in past studies, but is an essential step to constructing a precise fuzzy FNN (FFNN). Existing methods fuzzify all parameters at the … WebNov 18, 2015 · Next, the fuzzy neural network (FNN) model is trained by the proposed error back propagation training algorithm (EBPTA) scheme. After training of the model, physical parameters may be identified in fuzzified form if new maximum response data is supplied as input to the net which are also in fuzzified form. theoretical concepts of e-governance
(PDF) A Hybrid System Geno-Fuzzified Neural Network for
WebThe fuzzy inference process under Takagi-Sugeno Fuzzy Model (TS Method) works in the following way −. Step 1: Fuzzifying the inputs − Here, the inputs of the system are made fuzzy. Step 2: Applying the fuzzy operator − In this step, the fuzzy operators must be applied to get the output. WebNov 1, 1995 · We derive a general learning algorithm for training a fuzzified feedforward neural networks that has fuzzy inputs, fuzzy targets, and fuzzy conncetion weights. The derived algorithm is applicable to the learning of fuzzy connection weights with … WebNov 18, 2015 · This paper uses fuzzy neural network modelling for the identification of structural parameters of multi-storey shear buildings. First the identification has been … theoretical / conceptual framework