Forecasting bike sharing demand using fuzzy inference mechanism

Forecasting bike sharing demand is of paramount importance for management of fleet in city level. Rapidly changing demand in this service is due to a number of factors including workday, weekend, holiday and weather condition. These nonlinear dependencies make the prediction a difficult task. This work shows that type-1 and type-2 fuzzy inference-based prediction mechanisms can capture this highly variable trend with good accuracy. Wang-Mendel rule generation method is utilized to generate rule base and then only current information like date related information and weather condition is used to forecast bike share demand at any given point in future. Simulation results reveal that fuzzy inference predictors can potentially outperform traditional feed forward neural network in terms of prediction accuracy.

Uncoupling EPA and DHA in fish nutrition: dietary demand is limited in atlantic salmon and effectively met by DHA alone

Due to the scarcity of marine fish oil resources, the aquaculture industry is developing more efficient strategies for the utilization of dietary omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA). A better understanding of how fish utilize EPA and DHA, typically provided by fish oil, is needed. However, EPA and DHA have different physiological functions, may be metabolized and incorporated into tissues differently, and may vary in terms of their importance in meeting the fatty acid requirements of fish. To address these questions, Atlantic salmon were fed experimental diets containing, as the sole added dietary lipid source, fish oil (positive control), tallow (negative control), or tallow supplemented with EPA, DHA, or both fatty acids to ~50 or 100 % of their respective levels in the positive control diet. Following 14 weeks of feeding, the negative control diet yielded optimum growth performance. Though surprising, these results support the notion that Atlantic salmon requirements for n-3 LC-PUFA are quite low. EPA was largely β-oxidized and inefficiently deposited in tissues, and increasing dietary levels were associated with potential negative effects on growth. Conversely, DHA was completely spared from catabolism and very efficiently deposited into flesh. EPA bioconversion to DHA was largely influenced by substrate availability, with the presence of preformed DHA having little inhibitory effect. These results clearly indicate EPA and DHA are metabolized differently by Atlantic salmon, and suggest that the n-3 LC-PUFA dietary requirements of Atlantic salmon may be lower than reported and different, if originating primarily from EPA or DHA.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.