A single-server queueing model with infinite buffer and batch arrival of customers is considered. In contrast to the standard batch arrival when a whole batch arrives into the system at one epoch, we assume that the customers of an accepted batch arrive one by one in exponentially distributed times. Service time is exponentially distributed. Flow of batches is the stationary Poisson arrival pro...

In many practical situations in NN learning, training examples tend to be supplied one by one. In such situations, incremental learning seems more natural than batch learning in view of the learning methods of human beings. In this paper, we propose an incremental learning method in neural networks under the projection learning criterion. Although projection learning is a linear learning method...

The development of parallel batch pattern back propagation training algorithm of multilayer perceptron and its scalability research on general-purpose parallel computer are presented in this paper. The model of multilayer perceptron and batch pattern training algorithm are theoretically described. The algorithmic description of the parallel batch pattern training method is presented. The scalab...

A novel data-driven control performance assessment (CPA) method is proposed for batch processes controlled by iterative learning control (ILC) based on two-dimensional linear quadratic Gaussian (LQG) benchmark. Previous studies on CPA for ILC are based on an assumption that the model of the controlled batch process is known, whereas this study proposes a model-free CPA method. Based on the two-...

Stability data were generated by the Monte Carlo method, and batch-to-batch variability was evaluated by analysis of differences in slope and intercept according to the analysis of covariance (ANCOVA) approach recommended in the FDA Guidance. Using the same generated data, batch-to-batch variability was also evaluated by assessing the equivalence of shelf lives estimated for individual batches ...

The K-SVD algorithm is a highly effective method of training overcomplete dictionaries for sparse signal representation. In this report we discuss an efficient implementation of this algorithm, which both accelerates it and reduces its memory consumption. The two basic components of our implementation are the replacement of the exact SVD computation with a much quicker approximation, and the us...

We present a new solution approach for short-term planning of batch production, which decomposes the problem into batching and batch scheduling. Batching converts the primary requirements for products into individual batches. We formulate the batching problem as a mixed-integer nonlinear program, which can be solved by standard software. Batch scheduling allocates the batches to scarce resource...

Batch distillation processes are widely used in chemical industry. In this work, we consider the optimization of such processes by simulated annealing. Although this method is stochastically in nature, it has two evitable advantages: it can be readily connected to highly sophisticated simulation codes and it converges towards a global optimum. According to the characteristics of batch distillat...

1) There exists a study on employing mini-batch approach on SVRG, one of the VR methods. It shows that the approach cannot scale well that there is no significant difference between using 16 threads and more[2]. This study observes the cause of the poor scalability of this existing mini-batch approach on VR method. 2) The performance of mini-batch approach on distributed setting is improved by ...