[PDF] Top 20 Fuzzy and Neural Network Models for Analyses of Piles
Has 10000 "Fuzzy and Neural Network Models for Analyses of Piles" found on our website. Below are the top 20 most common "Fuzzy and Neural Network Models for Analyses of Piles".
Fuzzy and Neural Network Models for Analyses of Piles
... developed neural network, there is an input layer, where input data presented to the network and an output layer, which one neuron representing ultimate capacity, and also two hidden layers as ... See full document
224
The Prediction of Needle Penetration Force in Woven Denim Fabrics Using Soft Computing Models
... the fuzzy logic (FL) model. Moreover, the performance of fuzzy logic model is compared with that of the artificial neural network (ANN) ...the fuzzy logic model, the sewing needle size, ... See full document
11
To Design and Implement Neural Network and Fuzzy Logic for Software Development Effort Prediction
... a fuzzy logic for software development effort ...using fuzzy logic and neural network models and the results of fuzzy logic will be compared with RBNN based upon various ... See full document
6
Hybrid Models Performance Assessment to Predict Flow of Gamasyab River
... AR models (Kisi ...- neural network for the Ligvan Chai (Tabriz, Iran) catchment has been ...artificial neural network-Wavelet is performed, which shows that the model which ... See full document
10
Auto control of pumping operations in sewerage systems by rule based fuzzy neural networks
... water level gauging stations that belong to two sub-drainage systems. The learning target of the RNN model was the wa- ter level observations of YC10. To obtain 5- to 20-min-ahead water level predictions, this study ... See full document
12
Neural Network Application in Prediction of Axial Bearing Capacity of Driven Piles
... Artificial neural networks (ANN) have been used by researchers as a tool for the development of predictive models on various geotechnical problems including bearing capacity of ...optimal models ... See full document
5
Deduction of reservoir operating rules for application in global hydrological models
... cal models is the inclusion of anthropogenic impacts on the water cycle, such as caused by ...derive fuzzy rules describing the way a reservoir is ...artificial neural network capable of mim- ... See full document
21
Seasonal and year to year variation in the macroinvertebrate communities of New Zealand forest streams : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Ecology at Massey University, Palmerston North, New Zealand
... Stepwise regression and neural network analyses of channel and catchment characteristics were used to produce models predicting previously measured tracer particle data in 42 streams in [r] ... See full document
153
Soft Computing Stock Market Price Prediction for the Nigerian Stock Exchange
... computing models- Artificial Neural Network (ANN) and Fuzzy Artificial Neural Network (FANN) hybrid model were used to forecast the next day’s closing ... See full document
5
Motion learning by robot apprentices : a fuzzy neural approach
... Analytical resolution methods for fuzzy relational equations allow incremental on- line learning in fuzzy neural networks, which is an advantage over classic neural models.. However, the[r] ... See full document
226
Pseudo random number generator based on Neuro Fuzzy models
... a fuzzy process of MNNs has been used concerning the state-dependent memristor properties providing means for perceiving the complex MNNs by means of dual subsystems ...a neural network based ...This ... See full document
8
Performance Analyses of Recurrent Neural Network Models Exploited for Online Time-Varying Nonlinear Optimization
... recurrent neural network (RNN), i.e., the Zhang neural network (ZNN), is presented and investigated for online time-varying non- linear optimization ...ZNN models the- oretically via ... See full document
16
Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis
... a fuzzy if–then rule base so that a desired input/output mapping is ...a fuzzy rule base has received more attention ...optimizing fuzzy reason- ing via NN structures [15]–[21]. Parameters in ... See full document
11
Comparative study of static and dynamic neural network models for nonlinear time series forecasting
... optimal network estimated (trained) and obtained the output values of the system (the simulated values or out- of-sample ...gaussmf fuzzy inferencing system, the number of forecasting errors of the ... See full document
18
Comparative Study of Various Neural Network Models for Software Quality Estimation
... of models available to measure the software ...of neural network in software quality estimation based on object oriented ...three neural network models namely Ward Neural ... See full document
11
Neural Network Regressions with Fuzzy Clustering
... hybrid neural network regression models with unsupervised fuzzy clustering is proposed for clustering nonparametric regression models for ...the neural network regression ... See full document
6
Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods
... The periodogram of 9ltered data is shown in Figure 5. Figure 6 shows the power spectral density function of data, which passes through the notch 9lter. Also Figure 7 brieEy shows that the probability distribution density ... See full document
8
Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
... the Fuzzy method is its ability to work with approximate data and find explicit ...the Fuzzy set is an appropriate method for the analysis of complex systems and decision ...values, Fuzzy logic ... See full document
17
DEVELOPMENT AND APPLICATION OF A STAGE GATE PROCESS TO REDUCE THE UNERLYING RISKS OF IT SERVICE PROJECTS
... Affinity approach is used to classifying and prediction. Furthermore, affinity set is also used to investigating the relationship between output and inputs dataset [19]. Agarwal and Chen [20] proposed a predictive model ... See full document
10
Type-2 Fuzzy Logic Approach To Increase The Accuracy Of Software Development Effort Estimation
... useful models to predict the costs of software product ...type-2 fuzzy logic in which the gradient descend algorithm and the neuro- fuzzy-genetic hybrid approach have been used in order to teach the ... See full document
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