The second test focused on direct and iterative solutions of a large sparse system of linear equations. It showed identical results between the two packages, though it is important to note that the syntax and display of output between IDL and Matlab differs greatly. The first, a basic test inspired by the CIRC Tutorial for Basic Matlab, consisted of solving a system of linear equations using basic operations, computing eigenvalues and eigenvectors, and creating two-dimensional plots. Two studies were performed for this analysis. We compared the usability and efficiency of IDL to that of Matlab to determine if IDL is a viable substitute. Future work will focus on implementing advanced fuzzy inference techniques and GUI tools.Ĭreated by Exelis Visual Information Solutions, IDL (Interactive Data Language) is a commercial package used for data analysis. The OCTFRI Toolbox includes functions that enable the user to evaluate Fuzzy Inference Systems (FISs) from the command line and from OCTAVE scripts, read/write FISs and OBS to/from files, and produce a graphical visualisation of both the membership functions and the FIS outputs. The OCTAVE Fuzzy Rule Interpolation (OCTFRI) Toolbox is an open-source toolbox for OCTAVE programming language, providing a large functionally compatible subset of the MATLAB FRI toolbox as well as many extensions. The goal of this paper is to introduce some details of the adaptation of the FRI toolbox to support the GNU/OCTAVE programming language. The first FRI toolbox being able to handle different FRI methods was developed by Johanyak et. This cases the fuzzy model contains only the most relevant rules, without covering all the antecedent universes. Fuzzy control systems based on the Fuzzy Rule Interpolation (FRI) concept play a major role in different platforms, in case if only a sparse fuzzy rule-base is available. In most fuzzy control applications (applying classical fuzzy reasoning), the reasoning method requires a complete fuzzy rule-base, i.e all the possible observations must be covered by the antecedents of the fuzzy rules, which is not always available. For uses in teaching, the similarity of its syntax with Matlab will likely make textbook instructions directly applicable, and we thus recommend consideration of Octave for this purpose, though the particular assignments of each course will need to be tested in detail. Therefore, for uses in research, Octave's maturity and resulting richness of functions make it a viable alternative to Matlab. Another reason to consider Octave is that free parallel computing extensions are available that are known to work with this package. Based on these tests, we conclude that GNU Octave is the most compatible with Matlab due to its numerical abilities and the similarity of its syntax. The available graphical functions differ in functionality, but give equivalent plots, though FreeMat has limited three-dimensional graphics capabilities at present. All packages gave identical numerical results, though Scilab exhibited a limitation in the size of the linear system it could solve in the complex test problem and FreeMat was hampered by the lack of a conjugate gradient function. Then, we compare the results we receive from GNU Octave, FreeMat, and Scilab to our previously found Matlab results. In addition, we research a more complex test problem resulting from a finite difference discretization of the Poisson equation in two spatial dimensions. Are these packages viable alternatives to Matlab for uses in research or teaching? We review in depth the basic operations of Matlab, such as solving a system of linear equations, com-puting eigenvalues and eigenvectors, and plotting in two-dimensions. Unlike Matlab, the other three packages are free of charge to the user and easily available to download to Linux, Windows, or Mac OS X operating systems. GNU Octave, FreeMat, and Scilab are other numerical computational packages that have many of the same features as Matlab. specific growth rate = 0.04/hr), but i faced feed contamination after 4-5 days.The commercial software package Matlab is the most widely used numerical compu-tational package. Do I need to grow the cells at very low aeration rate? Should I do it in batch phase or chemostat? Earlier I planned to use chemostat at dilution rate of 0.015/hr ( max. But for me problem how to find out the limitation in case of fungal pellet. So my hypothesis is that compounds which I am getting is may be due to the oxygen limitation. The limitation starts when the pellet size is 0.077mm, which is very small. According to model suggested by Pirt, in pellet phase oxygen is more likely to be limiting growth nutrient. I have observed that most of the VOC are produced when the trichoderma grows in the forms of pellets (suspended form). I am trying to isolate the VOC volatile organic components by trichoderma fermentation.
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