38 research outputs found
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
Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines
We address a new numerical method based on a class of machine learning methods, the so-called Extreme Learning Machines (ELM) with both sigmoidal and radial-basis functions, for the computation of steady-state solutions and the construction of (one-dimensional) bifurcation diagrams of nonlinear partial differential equations (PDEs). For our illustrations, we considered two benchmark problems, namely (a) the one-dimensional viscous Burgers with both homogeneous (Dirichlet) and non-homogeneous boundary conditions, and, (b) the one- and two-dimensional Liouville–Bratu–Gelfand PDEs with homogeneous Dirichlet boundary conditions. For the one-dimensional Burgers and Bratu PDEs, exact analytical solutions are available and used for comparison purposes against the numerical derived solutions. Furthermore, the numerical efficiency (in terms of numerical accuracy, size of the grid and execution times) of the proposed numerical machine-learning method is compared against central finite differences (FD) and Galerkin weighted-residuals finite-element (FEM) methods. We show that the proposed numerical machine learning method outperforms in terms of numerical accuracy both FD and FEM methods for medium to large sized grids, while provides equivalent results with the FEM for low to medium sized grids; both methods (ELM and FEM) outperform the FD scheme. Furthermore, the computational times required with the proposed machine learning scheme were comparable and in particular slightly smaller than the ones required with FE
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
Tahap pengawalan pihak pengurusan politeknik dalam mengurangkan gejala sosial di kalangan pelajar
Kebelakangan ini gejala sosial di kalangan pelajar sarna ada di sekolah mahupun
di Institusi Pengajian Tinggi semakin membimbangkan kita. Benta-berita dan laporan
media massa dan elektronik tentang gejala sosial seperti berdua-duaan (coupling),
herpeleseran, bersekedudukan, membuang bayi dan pergaulan bebas kemp kah
dilaporkan dan semacam sudah menjadi perkara biasa Oleh itu kajian ini dijalankan
bertujuan untuk mengenalpasti tahap pengawalan dan tahap kejayaan pengawalan yang
dilakukan oleh pihak pengurusan politeknik dalam menangani gejala sosia1 di kalangan
pelajar. Kajian ini dijalankan di Politeknik Kota Bharu dan 100 orang pensyarahnya
dikenalpasti sebagai responden kajian. Data-data dianalisis menggunakan perisian
Statistical Package of Social Science (.SPSS) Versi 10.0 melibatkan peratusan dan purata
min. Dapatan kajian mendapati bahawa pihak pengurusan politeknik sudah melakukan
pengawalan yang tinggi dalam menangani masalah ini. Narnun mungkin disebabkan
faktor-faktor di luar kawalan maka gejala sosial dllihat masih berlaku di politeknik
walaupun ianya tidaklah berapa sen us
Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem
Nurse shortage, uncertain absenteeism and stress are the constituents of an unhealthy working environment in a hospital. These matters have impact on nurses' social lives and medication errors that threaten patients' safety, which lead to nurse turnover and low quality service. To address some of the issues, utilizing the existing nurses through an effective work schedule is the best alternative. However, there exists a problem of creating undesirable and non-stable nurse schedules for nurses' shift work. Thus, this research attempts to overcome these challenges by integrating components of a nurse scheduling and rescheduling problem which have normally been addressed separately in previous studies. However, when impromptu schedule changes are required and certain numbers of constraints need to be satisfied, there is a lack of flexibility element in most of scheduling and rescheduling approaches. By embedding the element, this gives a potential platform for enhancing the Evolutionary Algorithm (EA) which has been identified as the solution approach. Therefore, to minimize the constraint violations and make little but attentive changes
to a postulated schedule during a disruption, an integrated model of EA with Cuckoo Search (CS) is proposed. A concept of restriction enzyme is adapted in the CS. A total of 11 EA model variants were constructed with three new parent selections, two new crossovers, and a crossover-based retrieval operator, that specifically are theoretical contributions. The proposed EA with Discovery Rate Tournament and Cuckoo Search Restriction Enzyme Point Crossover (DᵣT_CSREP) model emerges
as the most effective in producing 100% feasible schedules with the minimum penalty value. Moreover, all tested disruptions were solved successfully through preretrieval and Cuckoo Search Restriction Enzyme Point Retrieval (CSREPᵣ) operators.
Consequently, the EA model is able to fulfill nurses' preferences, offer fair on-call delegation, better quality of shift changes for retrieval, and comprehension on the two-way dependency between scheduling and rescheduling by examining the
seriousness of disruptions