Roberto Lujić

An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices

The close price prediction model of the Zagreb Stock Exchange Crobex(R) index is presented in this paper. For the input/output data plan modeling the Crobex(R) index close price historical data are retrieved from the Zagreb Stock Exchange official internet pages. The prediction model is created in the way that for each of 5days in advance it predicts the Crobex(R) close price. The prediction model is generated based on the input/output data plan by means of the adaptive neuro-fuzzy inference system method, representing the fuzzy inference system. It is of the essence to point out that for each day a separate fuzzy inference system is created by means of the adaptive neuro-fuzzy inference system method based on the same set of input/output data, the only difference being that for every separate fuzzy inference system different subsets for training and checking are used so that input variables are differently created. The input/output data set represents the historical data of the Crobex(R) index close price from 4 November 2010 to 24 January 2012 and the Crobex(R) index close price is predicted for the subsequent 5days, the first day of prediction being 25 January 2012. After that the above mentioned input/output data set is shifted 5days in advance and the Crobex(R) index close price is predicted in advance for the next 5days starting with the last day of the input/output data set. In that way the Crobex(R) index close prices are predicted until 19 October 2012 based on the Crobex(R) index close price historical data. At the end of the paper qualitative and quantitative estimates are presented for the given approach of predicting the Crobex(R) index close price showing that the approach is useful for predicting within its limits.

Applying artificial intelligence to the scheduling problem in the ERP system

The basic enterprise tasks are how to satisfy customer demands, how to fulfil due dates, low prices and undoubted quality. Today most of Croatian enterprises are not able to fulfil those obligations. Usually, enterprises have inappropriate planning, scheduling and launching models. The paper shows structure of Croatian Enterprise Resource Planning System (ERP) that is developed for single and batches production and place of artificial intelligence technologies in development of new generation of EPR system. Through the application of 3-tournament steady-state selection genetic algorithm scheduling process can be improved and plan variants can be achieved according to cost, time or both. At the beginning were only some planning techniques such as line charts, line of balance, organisational appliances. The computer era began the first computer programs so called informational islands. These programs solved only particular problems like problems connected with accounting, bookkeeping or employees.

USE OF SOFT COMPUTING TECHNIQUE FOR MODELLING AND PREDICTION OF CNC GRINDING PROCESS

Due to the complexity of grinding process of multilayer ceramics, and the need for a specific product quality, the choice of optimal technological parameters is a challenging task for the manufacturers. The main aim of investigation is to secure the demanded final product quality (plane parallelism) in the function of input parameters (machine, machine operator, foil and production line). “Soft computing techniques” are becoming more interesting to the researchers for the modelling of processing parameters of complex technological processes. In this paper, a soft computing technique, known as the Artificial Neural Networks (ANN), is used for the modelling and prediction of parameters of technological process of CNC grinding of multilayer ceramics. The results show that the ANN with the back- propagation algorithm justifies the application also to this problem. By designing different architectures of ANN (learning rules, transfer functions, number and structure of hidden layers and other) on the set of data from the production - technological process, the best result of RMS error (10, 76 %) in the process of learning and 12, 07 % in the process of validation was achieved. The achieved results confirm the acceptability and the application of this investigation in the technological and operational preparation of production.

Application of expert systems for selection of installation pipes

The enterprises want to minimise the overall production costs, to maintain the quality and to increase the competitiveness on the market. It is possible to increase the number of customers, efficiency and effectiveness through the product quality. Therefore, every enterprise needs to have a good organisational structure, good experts and the knowledge how to do qualitative work. The paper shows the application of expert system to the problem of selection of installation pipes according to the given attributes. The input attributes are: mechanical load, material, colour, flame resistance, length, packaging, temperature range and outer diameter. The output parameter is the type of installation pipe. The expert system can give a second opinion, explicitly and in detail. They have the intelligent database and the possibility to adopt new knowledge, but also have a disadvantage that they use only the stored data to create solutions. Therefore, it is necessary to change the data according to a situation in the given environment. Despite the disadvantages of expert systems, their application is becoming increasingly important for the progress of enterprises.