PENERAPAN ALGORITMA OPTIMASI CHAOS PADA RIDGE POLYNOMIAL NEURAL NETWORK UNTUK PREDIKSI JUMLAH PENGANGGURAN (Studi kasus : Kalimantan Barat) - , RINA PRAMITASARI ([Yogyakarta] : Universitas Gadjah Mada, 2012)
Ridge polynomial neural network (RPNN) originally proposed by Shin and Ghosh, constructed from an increase in the number of order pi-sigma neurons (PSN). RPNN maintain fast learning, a strong mapping of a single layer higher order neural network (HONN) and avoid many of the weight due to the increased number of inputs. Chaos optimization algorithm is used to utilize the logistic equation is sensitive to initial conditions, so the chaotic movement can go in any situation in a certain scale in order, ergodic and maintain the diversity of solutions. Chaos optimization algorithm applied to RPNN and used for prediction of unemployment in West Kalimantan. Network training process using ridge polynomial neural network, while the search for initial values of weights and bias network using chaos optimization algorithm. Structure used consists of 6 neurons input layer and 1 neuron output layer. Data obtained from the Central Bureau of Statistics. The results of this study indicate that the proposed algorithm can be used for prediction.