PENENTUAN ARSITEKTUR JARINGAN SYARAF TIRUAN BACKPROPAGATION (BOBOT AWAL DAN BIAS AWAL) MENGGUNAKAN ALGORITMA GENETIKA - , christian dwi suhendra; , Drs. Retantyo Wardoyo, M,Sc.,Ph.D ([Yogyakarta] : Universitas Gadjah Mada, 2014)
The weakness of back propagation neural network is very slow to converge and local minima issues that makes artificial neural networks (ANN) are often being trapped in a local minima. A good combination between architecture, intial weight and bias are so important to overcome the weakness of backpropagation neural network. This study developed a method to determine the combination parameter of architectur, initial weight and bias. So far, trial and error is commonly used to select the combination of hidden layer, intial weight and bias. Initial weight and bias is used as a parameter in order to evaluate fitness value. Sum of squared error(SSE) is used to determine best individual. individual with the smallest SSE is the best individual. Best combination parameter of architecture, initial weight and bias will be used as a paramater in the backpropagation neural network learning. The results of this study is an alternative solution to solve the problems on the backpropagation learning that often have problems in determining the parameters of the learning. The result shows genetic algorithm method can provide a solution for backpropagation learning and can improve the accuracy, also reduce long learning when it compared with the parameters were determined manually.