Adaptive Neuro-Fuzzy Inference (ANFIS) Applications to PCP-Based NMDA Receptor Antagonists

2005-05-10
Büyükbingöl, Mehmet Erdem
Şişman Yılmaz, Arzu
Alpaslan, Ferda Nur
Adejare, Adaboye

Suggestions

Adaptive neuro-fuzzy inference system (ANFIS): A new approach to predictive modeling in QSAR applications: A study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists
Buyukbingol, Erdem; Sisman, Arzu; Akyıldız, Murat; Alpaslan, Ferda Nur; Adejare, Adeboye (Elsevier BV, 2007-06-15)
This paper proposes a new method, Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate physicochemical descriptors of certain chemical compounds for their appropriate biological activities in terms of QSAR models with the aid of artificial neural network (ANN) approach combined with the principle of fuzzy logic. The ANFIS was utilized to predict NMDA (N-methyl-D-Aspartate) receptor binding activities of phencyclidine (PCP) derivatives. A data set of 38 drug-like compounds was coded with 1244 calculated...
Adaptive neuro fuzzy inference system applications in chemical processes
Güner, Evren; Özgen, Canan; Leblebicioğlu, Kemal; Department of Chemical Engineering (2003)
Neuro-Fuzzy systems are the systems that neural networks (NN) are incorporated in fuzzy systems, which can use knowledge automatically by learning algorithms of NNs. They can be viewed as a mixture of local experts. Adaptive Neuro-Fuzzy inference system (ANFIS) is one of the examples of Neuro Fuzzy systems in which a fuzzy system is implemented in the framework of adaptive networks. ANFIS constructs an input-output mapping based both on human knowledge (in the form of fuzzy rules) and on generated input-out...
Adaptive model predictive control of uncertain systems with input constraints
Yayla, Metehan; Kutay, Ali Türker (2017-01-01)
© 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.This paper introduces a new hybrid adaptive model predictive control approach to control of uncertain dynamical systems where the matched uncertainty can be linearly parameterized by known basis functions. Introduced control framework respects the actuator position limit and actuator rate limit. Initially, an integration method in adaptive control is employed to identify the matched uncertainty in conjunction with Pseu...
Adaptive Oversampling for Imbalanced Data Classification
Ertekin Bolelli, Şeyda (2013-01-01)
Data imbalance is known to significantly hinder the generalization performance of supervised learning algorithms. A common strategy to overcome this challenge is synthetic oversampling, where synthetic minority class examples are generated to balance the distribution between the examples of the majority and minority classes. We present a novel adaptive oversampling algorithm, Virtual, that combines the benefits of oversampling and active learning. Unlike traditional resampling methods which require preproce...
Adaptive multivariate solution schemes for inverse electrocardiography problem
Onak, Önder Nazım; Serinağaoğlu Doğrusöz, Yeşim; Weber, Gerhard Wilhelm; Department of Scientific Computing (2018)
Electrocardiographic Imaging (ECGI) is an emerging medical imaging modality to visualizetheheart’selectricalactivity. Ithasapromisingpotentialfordiagnosingcardiac abnormalities and facilitate the planning and execution of necessary treatments. Visualizing heart’s electrical activity requires solving the ill-posed inverse electrocardiography (ECG) problem. Despite the considerable efforts and improvements in this field, there exist some limitations and challenges that hinder its application to daily clinical ...
Citation Formats
M. E. Büyükbingöl, A. Şişman Yılmaz, F. N. Alpaslan, and A. Adejare, “Adaptive Neuro-Fuzzy Inference (ANFIS) Applications to PCP-Based NMDA Receptor Antagonists,” 2005, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/77896.