43 research outputs found
Rangkaian Neural Dalam Peramalan Harga Minyak Kelapa Sawit
No full textKertas kerja ini membincangkan penggunaan rangkaian neural suap-kehadapan dengan satu aras tersembunyi digabungkan dengan algoritma rambatan balik dan didapati ia sesuai untuk memerihalkan data harga minyak sawit. Kajian awal yang telah dilakukan oleh Azme et al. [1] mendapati analisis regresi berganda kurang sesuai digunakan kerana masalah multikolineariti dalam data kajian. Lima harga minyak sayuran dunia iaitu minyak sawit mentah, minyak isirong, minyak kacang soya, minyak kelapa dan minyak biji sawi telah dianalisis. Dua model telah dicadangkan iaitu, NN1 dan NN2. Hasil kajian mendapati bahawa kedua-dua model telah menunjukkan prestasi yang tinggi dengan mencatatkan nilai pekali penentuan, R2 yang tinggi iaitu 0.938 dan 0.940 masingmasing. Umumnya, rangkaian neural berupaya menjadi satu kaedah alternatif sekiranya masalah multikolineariti wujud terhadap data yang dikaji
Application of statistical and neural network model for oil palm yield study
No full textThis thesis presents an exploratory study on modelling of oil palm (OP) yield using statistical and artificial neural network approach. Even though Malaysia is one of the largest producers of palm oil, research on modelling of OP yield is still at its infancy. This study began by exploring the commonly used statistical models for plant growth such as nonlinear growth model, multiple linear regression models and robust M regression model. Data used were OP yield growth data, foliar composition data and fertiliser treatments data, collected from seven stations in the inland and coastal areas provided by Malaysian Palm Oil Board (MPOB). Twelve nonlinear growth models were used. Initial study shows that logistic growth model gave the best fit for modelling OP yield. This study then explores the causality relationship between OP yield and foliar composition and the effect of nutrient balance ratio to OP yield. In improving the model, this study explores the use of neural network. The architecture of the neural network such as the combination activation functions, the learning rate, the number of hidden nodes, the momentum terms, the number of runs and outliers data on the neural network’s performance were also studied. Comparative studies between various models were carried out. The response surface analysis was used to determine the optimum combination of fertiliser in order to maximise OP yield. Saddle points occurred in the analysis and ridge analysis technique was used to overcome the saddle point problem with several alternative combinations fertiliser levels considered. Finally, profit analysis was performed to select and identify the fertiliser combination that may generate maximum yiel
Penggunaan model berstruktur linear dalam pembinaan indeks kualiti hidup di Malaysia.
No full textLinear structural relation or LISREL has been adopted to check the relationship between observable variables and latent variables, which reflect to the quality of life. The formation of measurement model and structural model has been able to explain the relation between observable variable and latent variables and between latent variables and latent variables. As many as 15 variables which represent three factors have been taken into consideration in the measurement of quality of life (QOL) index, i.e. socioeconomic factor, demography structure factor and family size formation factor. An example of index measurement has been presented and lastly the QOL index based on three factors is listed according to district and state. It has been found that 59.5\% of the districts in Peninsular are below the minimum level, whereas the Federal Territory is at the highest index and Kedah records the lowest index
Neural network in modeling Malaysian oil palm yield
No full textProblem statement: Forecasting of palm oil yield has become an important element in the management of oil palm industry for proper planning and decision making. The importance of yield forecasting has led us to explore modeling of palm oil yield for Malaysia using the most recent development of Artificial Neural Network (ANN). The main issue in yield forecasting is to predict the future value with the minimum error. Approach: Artificial neural networks are computing systems containing many interconnected nonlinear neurons, capable of extracting linear and nonlinear regularity in a given data set. It is an artificial intelligence model originally designed to replicate the human brain's learning process, a network with many elements or neurons that are connected by communications channels or connectors. The ANN can perform a particular function when certain values are assigned to the connections or weights between elements. In this study, a secondary data set from the Malaysian Palm Oil Board (MPOB) on the foliar nutrient composition, fertilizer trials and Fresh Fruit Bunch (FFB) yield were taken and analyzed. The foliar nutrient composition variables are the nitrogen N, phosphorus P, potassium K, calcium Ca and magnesium Mg concentration, while the fertilizer trials data are the N, P, K and Mg fertilizers and are measured in kg per palm per year. The foliar composition data was presented in the form of measured values whiles the fertilizer data in ordinal levels, from zero to three. Results: Two experiments were conducted to demonstrate the implementation ANN and for both experiment, the result demonstrated that the number of hidden nodes produces an effect to the overall forecast performance of the ANN architecture. From the first experiment, it shows that the number of runs does not affect the ANN performance, but changing the momentum to learning rates, due to shows a significant improvement in the forecast result. The experimental result will be in the form of statistical analysis, the best neural network performance, the residual analysis and the effect on the learning rate on the NN performance. Conclusion: This study showed that modeling of oil palm yield using neural network requires data to be prepared or modified to satisfy the requirement of the parameters involved. This analysis yields the conclusion that only the number of hidden nodes has a significant influence on the NN performance and there is no effect resulting from the number of runs or the momentum term value on the neural network's performance
Forecasting of crude palm oil price using hybridizing wavelet and group method of data handling model
No full textForecasting of Crude Palm Oil (CPO) is one of the most important and the largest vegetable oil traded in the world market. This study investigates the forecasting of Crude Palm Oil (CPO) price using a hybrid model of Group Method of Data Handling (GMDH) with wavelet decomposition. The original monthly data of CPO time series were decomposed into the spectral band. After that, these decomposed subseries were given as input time series data to GMDH model to forecast the CPO price of monthly time series data. The result performance of hybridized GMDH model is compared with the original GMDH model. The measurements results from the mean absolute error (MAE) and the root mean square error (RMSE) showed that the hybrid GMDH model with wavelet decomposition gives more accurate result of predictions compared with the original GMDH model.</jats:p
Effects of chemical reaction, heat and mass transfer on boundary layer flow over a porous wedge with heat radiation in the presence of suction or injection R.Kandasamy ∗ Abd.Wahid B. Md.Raj
On Robust Environmental Quality Indices
No full textThis paper discusses a formulation of new environmental quality indices, which
can be used for monitoring environmental as well as meteorological parameters.
The formulation of the indices is based on conventional and robust principal
component analysis, which gives the linear combination of environmental
parameters. Comparisons are made between the conventional principal
component analysis (PCA) indices and robust principal component analysis
(RPCA) indices. The results show that the RPCA gave a better alternative linear
combination. A numerical example on air quality was used to illustrate the
application of the robust environmental indices
Pemodelan harga minyak sayuran menggunakan analisis regresi linear berganda.
No full textThis study focused on application of multiple regression in modeling vegetable oil prices. Five vegetable oil prices, namely CPO, SBO, CNO, PKO, and RSO have been analysed using monthly oil price data from year 2000. We found that multiple linear regression gave the value of 0.887, meaning 88.7\% of variance in CPO price could be explained by RSO, PKO, and CNO. The -test showed that the parameter estimates is significant at one percent level. This study concluded that multicollinearity and autocorrelation were detected inmuliple linear regression and are needed to be considered in further research
Fuzzy Entire Sequence Spaces
No full textWe first investigate the notion of fuzzy entire sequence space with a suitable example. Also we deal with the properties of the space of fuzzy entire sequences. The concepts of subset and superset of the fuzzy entire sequence spaces are introduced and their properties are discussed
Effects of chemical reaction, heat and mass transfer on boundary layer flow over a porous wedge with heat radiation in the presence of suction or injection
No full text
