We present a system identification method for problems with partially missing inputs and outputs. The method is based on a subspace formulation and uses the nuclear norm heuristic for structured low-rank matrix approximation, with the missing input and output values as the optimization variables. We also present a fast implementation of the alternating direction method of multipliers (ADMM) to ...

In this paper, a data imputation method with a Support Vector Machine (SVM) is proposed to solve the issue of missing data in activity-based diaries. Here two SVM models are established to predict the missing elements of ‘number of cars’ and ‘driver license’. The inputs of the former SVM model include five variables (Household composition, household income, Age oldest household member, Children...

In the conventional data envelopment analysis (DEA), it is assumed that all decision making units (DMUs) using the same input and output measures, means that DMUs are homogeneous. In some settings, however, this usual assumption of DEA might be violated. A related problem is the problem of textit{missing} textit{data} where a DMU produces a certain output or consumes a certain input but the val...

Fuzzy heterogeneous networks are recently introduced feed-forward neural network models composed of neurons of a general class whose inputs and weights are mixtures of continuous variables (crisp and/or fuzzy) with discrete quantities, also admitting missing data. These networks have net input functions based on similarity relations between the inputs to and the weights of a neuron. They thus a...

In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neuron models accepting heterogeneous inputs in the form of mixtures of continuous (crisp and/or fuzz...

Propagating input uncertainty through non-linear Gaussian process (GP) mappings is intractable. This hinders the task of training GPs using uncertain and partially observed inputs. In this paper we refer to this task as “semi-described learning”. We then introduce a GP framework that solves both, the semi-described and the semi-supervised learning problems (where missing values occur in the out...

Most computer programs are brittle. They may have acceptable behavior for the range of applications that they are specified to work for, but they fail miserably for applications even slightly outside of that range. For example, a program may work well when its inputs are complete, but be unable to produce any sensible result if any of its inputs are missing or

since many time series are not normal, it is required to normalize data by transformation functions prior to any analysis and modeling. in this study, the next month rainfall of abadeh county station was predicted using the average monthly rainfall, minimum and maximum temperatures and minimum and maximum humidity as inputs of mlp network, both normally and raw, at period 1976 to 2013. after sc...

Missing values in inputs, outputs cannot be handled by the original data envelopment analysis (DEA) models. In this paper we introduce an approach based on interval DEA that allows the evaluation of the units with missing values along with the other units with available crisp data. The missing values are replaced by intervals in which the unknown values are likely to belong. The constant bounds...