Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this paper, a novel application of biologically inspired computing paradigm is presented for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by exploiting the competency of accurate modeling with feed forward artificial neural network (FF-ANN), global search efficacy of genetic algorithms (GA) and rapid local search with sequential quadratic programming (SQP). The fitness function for IVP of associated nonlinear RL circuit is developed by exploiting the approximation theory in mean squared error sense using an approximate FF-ANN model. Training of the networks is conducted by integrated computational heuristic based on GA-aided with SQP, i.e., GA-SQP. The designed methodology is evaluated to variants of nonlinear RL systems based on both AC and DC excitations for number of scenarios with different voltages, resistances and inductance parameters. The comparative studies of the proposed results with Adam’s numerical solutions in terms of various performance measures verify the accuracy of the scheme. Results of statistics based on Monte-Carlo simulations validate the accuracy, convergence, stability and robustness of the designed scheme for solving problem in nonlinear circuit theory

Design of neuro-swarming computational solver for the fractional Bagley–Torvik mathematical model

This study is to introduce a novel design and implementation of a neuro-swarming computational numerical procedure for numerical treatment of the fractional Bagley–Torvik mathematical model (FBTMM). The optimization procedures based on the global search with particle swarm optimization (PSO) and local search via active-set approach (ASA), while Mayer wavelet kernel-based activation function used in neural network (MWNNs) modeling, i.e., MWNN-PSOASA, to solve the FBTMM. The efficiency of the proposed stochastic solver MWNN-GAASA is utilized to solve three different variants based on the fractional order of the FBTMM. For the meticulousness of the stochastic solver MWNN-PSOASA, the obtained and exact solutions are compared for each variant of the FBTMM with reasonable accuracy. For the reliability of the stochastic solver MWNN-PSOASA, the statistical investigations are provided based on the stability, robustness, accuracy and convergence metrics.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This paper has been partially supported by Fundación Séneca de la Región de Murcia grant numbers 20783/PI/18, and Ministerio de Ciencia, Innovación y Universidades grant number PGC2018-0971-B-100

Design of neuro-computing paradigms for nonlinear nanofluidic systems of MHD Jeffery–Hamel flow

© 2018 Taiwan Institute of Chemical Engineers In this paper, a neuro-heuristic technique by incorporating artificial neural network models (NNMs) optimized with sequential quadratic programming (SQP) is proposed to solve the dynamics of nanofluidics system based on magneto-hydrodynamic (MHD) Jeffery–Hamel (JHF) problem involving nano-meterials. Original partial differential equations associated with MHD–JHF are transformed into third order ordinary differential equations based model. Furthermore, the transformed system has been implemented by the differential equation NNMs (DE-NNMs) which are constructed by a defined error function using log-sigmoid, radial basis and tan-sigmoid windowing kernels. The parameters of DE-NNM of nanofluidics system are optimized with SQP algorithm. To illustrate the performance of the proposed system, MHD–JHF models with base-fluid water mixed with alumina, silver and copper nanoparticles for different Hartman numbers, Reynolds numbers, angles of the channel and volume fractions with three different proposed DE-NNMs are designed to evaluate. For comparison purpose, the proposed results with reference numerical solutions of Adams solver illustrate their worth. Statistical inferences through different performance indices are given to demostrate the accuracy, stability and robustness of the stochastic solvers

D˙IFERANS˙IYEL DENKLEMLER˙IN YAPAY S˙IN˙IR AGLARI ˘ ˙ILE NÜMER˙IK ÇÖZÜMLER˙I

D˙IFERANS˙IYEL DENKLEMLER˙IN YAPAY S˙IN˙IR AGLARI ˘ ˙ILE NÜMER˙IK ÇÖZÜMLER˙I ˙Iclal GÖR Doktora Tezi, Matematik Anabilim Dalı Tez Danı¸smanı: Dr. Ögr. Üyesi Korhan GÜNEL ˘ 2020, 91 sayfa Bu çalı¸smada, birinci ve ikinci mertebeden lineer ba¸slangıç deger problemleri, ˘ Dirichlet sınır ko¸sulları içeren ikinci mertebeden lineer ve lineer olmayan diferansiyel denklemler ve birinci mertebeden lineer diferansiyel denklem sistemlerinin nümerik çözümleri ileri beslemeli tek ara katmanlı yapay sinir agları ˘ kullanılarak elde edilmi¸stir. Problemlerin çözümleri için modellenen sinir agları, popülasyon tabanlı global ˘ optimizasyon metotlarından Parçacık Sürü Optimizasyonu, Kütle Çekim Arama Algoritması, Yapay Arı Koloni Algoritması ve Karınca Koloni Optimizasyonu kullanılarak egitilmi¸stir. Ek olarak bahsi geçen optimizasyon algoritmaları ˘ Parçacık Sürü Optimizasyonu algoritması ile hibritlenerek çözümler elde edilmi¸stir. Tez çalı¸sması boyunca incelenen optimizasyon yakla¸sımlarından elde edilen izlenimler dogrultusunda, bilinen en iyi çözümün kom¸sulu ˘ gunda üretilen ˘ hiper-küreleri kullanan yeni bir mutasyon operatörü tanımlanmı¸stır. Deneysel çalı¸smalarda elde edilen bulgular, adi diferansiyel denklemlerin nümerik çözümlerini elde etmede yapay sinir agı kullanımının geleneksel iterasyon tabanlı ˘ yöntemlere göre iyi bir alternatif olabilecegini göstermi¸stir. Yapay sinir a ˘ glarının, ˘ çözüm aranan aralıgın her noktasında tahmini bir de ˘ ger üretebilme yetenekleri bu ˘ yöntemleri klasik yöntemlere göre tercih edilebilir hale getirmektedir. Tezde önerilen yakla¸sım, farklı sabit adım uzunlukları için degi¸sik tipteki ˘ diferansiyel denklemler üzerinde test edilmi¸s ve diger yöntemlerle kıyaslandı ˘ gında ˘ genel olarak benzer veya çogu zaman daha iyi sonuç vermi¸stir. Bununla birlikte, ˘ her tipte diferansiyel denklemi çözebilecek evrensel bir yapay sinir agı modeli ˘ olu¸sturmanın olası olmadıgı kanısına varılmı¸stır.˙IÇ˙INDEK˙ILER KABUL VE ONAY SAYFASI . . . . . . . . . . . . . . . . . . . . . . . . iii B˙IL˙IMSEL ET˙IK B˙ILD˙IR˙IM SAYFASI . . . . . . . . . . . . . . . . . . . v ÖZET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix ÖNSÖZ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi S˙IMGELER D˙IZ˙IN˙I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv ¸SEK˙ILLER D˙IZ˙IN˙I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii Ç˙IZELGELER D˙IZ˙IN˙I . . . . . . . . . . . . . . . . . . . . . . . . . . . xix 1. G˙IR˙I ¸S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. MATERYAL VE METOT . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1. ˙Ileri Beslemeli Yapay Sinir Agları ile Diferansiyel Denklemlerin ˘ Nümerik Çözümleri . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2. Diferansiyel Denklem Sistemlerinin Çözümleri . . . . . . . . . . . . 15 2.3. Popülasyon Tabanlı Global Optimizasyon Yakla¸sımları . . . . . . . . 17 2.3.1. Parçacık Sürü Optimizasyonu . . . . . . . . . . . . . . . . . . . . 18 2.3.2. Kütle Çekim Arama Algoritması . . . . . . . . . . . . . . . . . . . 21 2.3.3. Yapay Arı Koloni Algoritması . . . . . . . . . . . . . . . . . . . . 25 2.3.3.1. Yapay Arı Koloni Algoritması için Yeni Bir Mutasyon Önerisi . . . 28 2.3.4. Karınca Koloni Optimizasyonu . . . . . . . . . . . . . . . . . . . 31 3. DENEYSEL ÇALI ¸SMALAR . . . . . . . . . . . . . . . . . . . . . . . 37 4. TARTI ¸SMA VE SONUÇ . . . . . . . . . . . . . . . . . . . . . . . . . 70 KAYNAKLAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 EKLER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 A. EKLER D˙IZ˙IN˙I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 ÖZGEÇM˙I ¸S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide

Time Localization of Abrupt Changes in Cutting Process using Hilbert Huang Transform

Cutting process is extremely dynamical process influenced by different phenomena such as chip formation, dynamical responses and condition of machining system elements. Different phenomena in cutting zone have signatures in different frequency bands in signal acquired during process monitoring. The time localization of signal’s frequency content is very important. An emerging technique for simultaneous analysis of the signal in time and frequency domain that can be used for time localization of frequency is Hilbert Huang Transform (HHT). It is based on empirical mode decomposition (EMD) of the signal into intrinsic mode functions (IMFs) as simple oscillatory modes. IMFs obtained using EMD can be processed using Hilbert Transform and instantaneous frequency of the signal can be computed. This paper gives a methodology for time localization of cutting process stop during intermittent turning. Cutting process stop leads to abrupt changes in acquired signal correlated to certain frequency band. The frequency band related to abrupt changes is localized in time using HHT. The potentials and limitations of HHT application in machining process monitoring are shown