# Computational Intelligence Assignment

where N is the cardinality of the data set.System Inputs and OutputsLet the temperature and total demand of electricity at time instant t be T(t) and D(t),respectively. The goal of the fuzzy forecasting system is to predict the RRP price by usingsome historical data as system inputs. In this assignment, the historical data set used forbuilding the fuzzy system at time instant t is composed of a subset of the set M={T(t-2),T(t-1), T(t), D(t-2), D(t-1), D(t)}. The output of your system at time instant t is a forecastingvalue of the Recommended Retail Price (RRP) of electricity at the next time instant t+1,denoted by P(t+1).Note that you should select a subset of the set M as the system’s input variables by usingcorrelation analysis.Tasks Description (the maximum marks for each item below is 20)• Remove outliers of the output variable from the datasets (both training and test),and give a list of the outliers; and then rebuild the training and the test datasets;• Select appropriate values or fuzzy subsets for linguistic variables used in yourfuzzy rules;3• List the fuzzy rules that are generated by using statistical analysis (correlationcoefficients) with heuristics;• Implement your fuzzy system in Python, where all membership functions involvedin your system should be plotted clearly;• Report your system performance in terms of the average relative error for bothtraining and testing datasets, and analyze the effects of membership functions anddefuzzification methods.Remarks• Assessment will be done by looking at the average relative prediction accuracy forboth the training data set and the test data set.• Either Mamdani-type or Sugeno-type fuzzy rules can be applied.• Your report should provide a full list of Python codes used in your system withsome graphical illustrations. It will be appreciated to show some fine-tuning of thesystem’s parameters to produce sensible results. It is encouraged to appropriatelyuse appendices to detail your results.Assessment Criteria(100-80 marks) – An excellent, well-written report. You have produced a working systemthat produces sensible results. The report summarises the approach taken well. You haveanalysed the performance of the system and presented the results in an interesting andsound way. A thorough and systematic analysis of the effect of different membershipfunctions and different defuzzification techniques is presented.(79-60 marks) – A well-written report. You have produced a working system thatproduces good results. You have exhibited some initiative in the approach taken and theresults are presented clearly. An analysis of the effect of different membership functionsand different defuzzification techniques is presented.(59-40 marks) – A reasonable report that presents an account of the approach taken andthe final system. The system performs reasonably well and the results are presentedreasonably clearly. Either different membership functions or different defuzzificationtechniques have been explored.(39-20 marks) – A report that presents some results of a working system. Demonstratingsome understandings on fuzzy forecasting system design.(19-0 marks) – Either no report submitted or a report that shows little or no understandingof how to develop a fuzzy system.– End of Assignment Paper –

Assignment status: Already Solved By Our Experts

(USA, AUS, UK & CA  PhD. Writers)

QUALITY: 100% ORIGINAL PAPERNO PLAGIARISM - CUSTOM PAPER