This paper describes our strategy to approach reinforcement learning in robotic domains including the use of neural networks. We summarize our recent work on model-based reinforcement learning where models of hierarchical dynamic system are learned with stochastic neural networks [Yamaguchi and Atkeson, 2016b], and actions are planned with stochastic differential dynamic programming [Yamaguchi ...

accurate estimation of evaporation is important for design, planning and operation of water systems. in arid zones where water resources are scarce, the estimation of this loss becomes more interesting in the planning and management of irrigation practices. this paper investigates the ability of artificial neural networks (anns) technique to improve the accuracy of daily evaporation estimation....

background: we designed an algorithmic model based on the logistic regression analysis and a non-algorithmic model based on the artificial neural network (ann). materials and methods: the ability of these models was compared together in clinical application to differentiate malignant from benign breast tumors in a study group of 161 patients' records. each patient’s record consisted of 6 s...

this paper presents the prediction of vehicle's velocity time series using neural networks. for this purpose, driving data is firstly collected in real world traffic conditions in the city of tehran using advance vehicle location devices installed on private cars. a multi-layer perceptron network is then designed for driving time series forecasting. in addition, the results of this study a...

the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

in general, amount of sludge will definitely increase in near future and composting processes, optimum composting conditions and compost use as fertilizer and soil amendment will then be significant research topics. the present study was conducted for o2 parameter estimation by multiple regression and artificial neural networks methods. daily temperature, ch4, h2s, co2 and o2 measurements were ...

today, stock investment has become an important mean of national finance. apparently, it is significant for investors to estimate the stock price and select the trading chance accurately in advance, which will bring high return to stockholders. in the past, long-term trading processes and many technical analysis methods for stock market were put forward. however, stock market is a nonlinear sys...

This paper presents a parameter modeling to Power System Stabilizers (PSS) using MLP and RBF neural networks. The application of neural networks in PSS aims to improve the dynamic stability of electric power systems by reducing the machine eletromechanic damping oscillation when a disturbance occurs. According to the current plant operating conditions, the PSS parameters are automatically adjus...

This paper investigates some properties of Takagi-Sugeno (T-S) fuzzy Hopfield neural networks. First, we prove that there exists a unique solution of the T-S fuzzy Hopfield neural network. Second, we determine a condition for input-to-state stability (ISS) of the T-S fuzzy Hopfield neural network. These results will be useful to analyze dynamic behavior of fuzzy neural networks.

classification of vegetation according to their species composition is one of the most important tasks in the application of remote sensing in precision agriculture. to prepare an algorithm for such a mandate, there is a need for ground truth. field operation is very costly and time consuming. therefore, some other method must be developed, such as extracting information from the satellite imag...