Uemura [1] discovered a mapping formula that transforms and maps the state of nature into fuzzy events with a membership function that expresses the degree of attribution. In decision theory in no-data problems, seque...Uemura [1] discovered a mapping formula that transforms and maps the state of nature into fuzzy events with a membership function that expresses the degree of attribution. In decision theory in no-data problems, sequential Bayesian inference is an example of this mapping formula, and Hori et al. [2] made the mapping formula multidimensional, introduced the concept of time, to Markov (decision) processes in fuzzy events under ergodic conditions, and derived stochastic differential equations in fuzzy events, although in reverse. In this paper, we focus on type 2 fuzzy. First, assuming that Type 2 Fuzzy Events are transformed and mapped onto the state of nature by a quadratic mapping formula that simultaneously considers longitudinal and transverse ambiguity, the joint stochastic differential equation representing these two ambiguities can be applied to possibility principal factor analysis if the weights of the equations are orthogonal. This indicates that the type 2 fuzzy is a two-dimensional possibility multivariate error model with longitudinal and transverse directions. Also, when the weights are oblique, it is a general possibility oblique factor analysis. Therefore, an example of type 2 fuzzy system theory is the possibility factor analysis. Furthermore, we show the initial and stopping condition on possibility factor rotation, on the base of possibility theory.展开更多
Uemura [1] discovered the mapping formula for Type 1 Vague events and presented an alternative problem as an example of its application. Since it is well known that the alternative problem leads to sequential Bayesian...Uemura [1] discovered the mapping formula for Type 1 Vague events and presented an alternative problem as an example of its application. Since it is well known that the alternative problem leads to sequential Bayesian inference, the flow of subsequent research was to make the mapping formula multidimensional, to introduce the concept of time, and to derive a Markov (decision) process. Furthermore, we formulated stochastic differential equations to derive them [2]. This paper refers to type 2 vague events based on a second-order mapping equation. This quadratic mapping formula gives a certain rotation named as possibility principal factor rotation by transforming a non-mapping function by a relation between two mapping functions. In addition, the derivation of the Type 2 Complex Markov process and the initial and stopping conditions in this rotation are mentioned. .展开更多
A large majority of Kenyans still rely on traditional fuels to meet their domestic cooking needs. The demand for traditional biomass is therefore likely to increase in the coming decades as long as they remain the mos...A large majority of Kenyans still rely on traditional fuels to meet their domestic cooking needs. The demand for traditional biomass is therefore likely to increase in the coming decades as long as they remain the most readily available and affordable in comparison to modern energy options. This research sought to analyze the household use of traditional fuels and its possible contribution to deforestation in Kisii County. The willingness of respondents to adopt alternative biofuels and energy efficient stoves and barriers encountered were also assessed. Two structured questionnaires that contained both open and close-ended questions were administered to 436 households and 40 wood fuel sellers respectively. Analysis of variance and regression analysis were used to analyze the alternative hypotheses of the study. It was established that the use of charcoal was the most prevalent compared to other fuels. Household consumption of traditional fuels contributed to an estimated loss of 39 ha of forest cover per annum. However, since 89.7% of the wood fuel used was sourced from other counties, the loss of biomass did not occur in Kisii County. Given a chance, about 63% of the respondents were willing to adopt alternative biofuels and energy efficient stoves. However, the greatest barrier to the adoption of these alternatives was the high cost of purchase. Other barriers identified included lack of government support and unwillingness to let go of traditional cooking practices. It was recommended that the Kenyan government and other stakeholders should promote local technologies for producing energy efficient stoves to make them more affordable to the populace.展开更多
文摘Uemura [1] discovered a mapping formula that transforms and maps the state of nature into fuzzy events with a membership function that expresses the degree of attribution. In decision theory in no-data problems, sequential Bayesian inference is an example of this mapping formula, and Hori et al. [2] made the mapping formula multidimensional, introduced the concept of time, to Markov (decision) processes in fuzzy events under ergodic conditions, and derived stochastic differential equations in fuzzy events, although in reverse. In this paper, we focus on type 2 fuzzy. First, assuming that Type 2 Fuzzy Events are transformed and mapped onto the state of nature by a quadratic mapping formula that simultaneously considers longitudinal and transverse ambiguity, the joint stochastic differential equation representing these two ambiguities can be applied to possibility principal factor analysis if the weights of the equations are orthogonal. This indicates that the type 2 fuzzy is a two-dimensional possibility multivariate error model with longitudinal and transverse directions. Also, when the weights are oblique, it is a general possibility oblique factor analysis. Therefore, an example of type 2 fuzzy system theory is the possibility factor analysis. Furthermore, we show the initial and stopping condition on possibility factor rotation, on the base of possibility theory.
文摘Uemura [1] discovered the mapping formula for Type 1 Vague events and presented an alternative problem as an example of its application. Since it is well known that the alternative problem leads to sequential Bayesian inference, the flow of subsequent research was to make the mapping formula multidimensional, to introduce the concept of time, and to derive a Markov (decision) process. Furthermore, we formulated stochastic differential equations to derive them [2]. This paper refers to type 2 vague events based on a second-order mapping equation. This quadratic mapping formula gives a certain rotation named as possibility principal factor rotation by transforming a non-mapping function by a relation between two mapping functions. In addition, the derivation of the Type 2 Complex Markov process and the initial and stopping conditions in this rotation are mentioned. .
文摘A large majority of Kenyans still rely on traditional fuels to meet their domestic cooking needs. The demand for traditional biomass is therefore likely to increase in the coming decades as long as they remain the most readily available and affordable in comparison to modern energy options. This research sought to analyze the household use of traditional fuels and its possible contribution to deforestation in Kisii County. The willingness of respondents to adopt alternative biofuels and energy efficient stoves and barriers encountered were also assessed. Two structured questionnaires that contained both open and close-ended questions were administered to 436 households and 40 wood fuel sellers respectively. Analysis of variance and regression analysis were used to analyze the alternative hypotheses of the study. It was established that the use of charcoal was the most prevalent compared to other fuels. Household consumption of traditional fuels contributed to an estimated loss of 39 ha of forest cover per annum. However, since 89.7% of the wood fuel used was sourced from other counties, the loss of biomass did not occur in Kisii County. Given a chance, about 63% of the respondents were willing to adopt alternative biofuels and energy efficient stoves. However, the greatest barrier to the adoption of these alternatives was the high cost of purchase. Other barriers identified included lack of government support and unwillingness to let go of traditional cooking practices. It was recommended that the Kenyan government and other stakeholders should promote local technologies for producing energy efficient stoves to make them more affordable to the populace.
