in this study artificial neural network (ann) was used for modeling of wastewater treatment plants with using. for this purpose, the base of the quality parameters measured at the plant input, plant output value was predicted. neural network input data, including temperature (t), biochemical oxygen demand (bod), chemical oxygen demand (cod), total suspended solids (tss), total solids (ts) and p...

An arti...cial neural network model is used to predict the performance of Sino-foreign joint ventures. Performance of international joint ventures remains a relatively under-researched area, yet its importance is well recognized due to the tremendous surge in joint venture activities in the past decade. Data on 2,416 Sino-foreign joint ventures was gathered, allowing for empirical analysis usin...

Statistical modeling technique has pivotal role in better understanding of the software development processes. Among them neural network techniques have enhanced predictive capability than most other statistical models. This paper explains the application of principal component analysis to neural network modeling as a way to improve predictability of neural network. The purpose of principal com...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

this study employs a gmdh neural network model, which has high capability in recognition of complicated non-linear trends especially with small samples, for modeling and predicting iranian gdp growth. first a fundamental model containing 7 independent variables together with dependent variable is designed and then by using deductive process and omission of one variable at a time, a total of 18 ...

We present a theoretical and computational work, aiming at the estimation of firing rate based excitatory inhibitory neural network from realistic stimulus-response data. The stimulus response recordings are taken previous study which performs measurement on H1 neurons order Diptera flies. parameter is performed by maximum likelihood method. As data single recording 20 minutes, it segmented ind...

We study some aspects of the dynamic neural .lter (DNF), a recurrent network that produces spatiotemporal sequences in reaction to sets of constant inputs. The biological motivation for this study came from the observation of spatiotemporal patterns in the locust antennal lobe. Some of the aspects of these results can be reformulated and characterized by the DNF. Studying deterministic dynamics...

Introduction Conclusions References

Multi-layered neural networks have recently been proposed for nonlinear prediction and system modeling. Although proven successful for modeling time invariant nonlinear systems, the inability of neural networks to characterize temporal variability has so far been an obstacle in applying them to complicated non stationary signals, such as speech. In this paper we present a network architecture, ...