The aim of our project was to build an autonomous system, the main purpose of which is the care of garden plants, using mostly commercially available open source hardware. Nowadays, many shops offer simple watering systems, but often these are only pumps that periodically switch on and off at an adjustable time. We have built our system on a slightly different basis. It irrigates only when it is necessary. The main components of the system are therefore, in addition to pumps, soil moisture sensors. and Cílem našeho projektu bylo postavit autonomní systém, jehož hlavním účelem je péče o zahradní rostlinstvo, a to s využitím převážně běžně dostupného open source hardwaru. V dnešní době mnohé obchody nabízejí jednoduché zavlažovací systémy, ale často se jedná pouze o čerpadla, která se v nastavitelném čase periodicky spínají. Náš systém jsme vybudovali na trochu jiném základě. Zaléváme pouze tehdy, když je to třeba. Jedny z hlavních komponent systému proto tvoří, kromě čerpadel, také senzory půdní vlhkosti.
A fuzzy model based on an enhanced supervised fuzzy clustering algorithm is presented in this paper. The supervised fuzzy clustering algorithm [6] allows each rule to represent more than one output with different probabilities for each output. This algorithm implements k-means to initialize the fuzzy model. However, the main drawbacks of this approach are that the number of clusters is unknown and the initial positions of clusters are randomly generated. In this work, the initialization is done by the global k-means algorithm [1], which can autonomously determine the actual number of clusters needed and give a deterministic clustering result. In addition, the fast global k-means algorithm [1] is presented to improve the computation time. The model is tested on medical diagnosis benchmark data and Westland vibration data. The results obtained show that the model that uses the global k-means clustering algorithm [1] has higher accuracy when compared to a model that uses the k-means clustering algorithm. Besides that, the fast global k-means algorithm [1] also improved the computation time without degrading much the model performance.
In recent years the interest of the investors in efficient methods for the forecasting price trend of a share in financial markets has grown steadily. The aim is to accurately forecast the future behavior of the market in order to identificate the so-called "correct timing".
In this paper we analyze three different approaches for forecasting financial data: Autoregression, artificial neural networks and support vector machines and we will determine potentials and limits of these methods. Application to the Italian financial market is also presented.