Cascade-correlation (cascor) networks grow by recruiting hidden units to adjust their computational power to the task being learned. The standard cascor algorithm recruits each hidden unit on a new layer, creating deep networks. In contrast, the flat cascor variant adds all recruited hidden units on a single hidden layer. Student-teacher network approximation tasks were used to investigate the ...

Artificial neural networks are an in silico laboratory for studying the dynamics of the brain. In recurrent networks, the units’ activations are recurrently fed back into the network. Thereby complex network dynamics emerge that extend over longer time scales than the individual units’ activation time constants. The recurrent echo-state networks with their fixed connection weights acquire an in...

Conventional data envelopment analysis (DEA) assists decision makers in distinguishing between efficient and inefficient decisionmaking units (DMUs) in a homogeneous group. However, DEA does not provide more information about the efficient DMUs. This research proposes a methodology to determine one common set of weights for the performance indices of only DEA efficient DMUs. Then, these DMUs ar...

One may employ Data Envelopment Analysis (DEA) to discriminate decision-making units (DMUs) into efficient and inefficient ones base upon the multiple inputs and output performance indices. In this paper we consider that there is a centralized decision maker (DM) who ‘owns’ or ‘supervises’ all the DMUs. In such intraorganizational scenario the DM has an interest in discriminating the efficient ...

The problem of determining the proper size of an artificial neural network is recognized to be crucial, especially for its practical implications in such important issues as learning and generalization. One popular approach for tackling this problem is commonly known as pruning and it consists of training a larger than necessary network and then removing unnecessary weights/nodes. In this paper...

An experiment was conducted to study the performance of broilers chicks (2 to 42 d of age) fed diets containing pearl millet (PM, Pennisetum typhoides), foxtail millet (FOM, Setaria italica) or finger millet (FIM, Elusine coracana) totally replacing (w/w) yellow maize (YM) with and with out supplementing non-starch polysaccharide (NSP) hydrolysing enzymes at the rate of 0.5 g/kg diet. Enzyme pr...

In order to evaluate the performance of the enterprise effectively and reasonably, a performance evaluation model is proposed based on optimized DEA algorithm with decision making units. First, a second goal is set on the basis of classic DEA algorithm, and the only public weight is further determined to make the minimizing sum of changes between the efficiency of public weights and the classic...

Data Envelopment Analysis is a linear programming technique for assessing the efficiency and productivity of decision making units (DMUs). Over the last decade, DEA has gained considerable attention as a managerial tool for measuring performance. The flexibility in selecting the weights in standard DEA models deters the comparison among DMUs on a common base. Moreover, these weights are unsuita...

Data envelopment analysis (DEA) is a technique for performance evaluation of peer decision making units (DMUs). The network DEA models study the internal structures of DMUs. Using two-stage network structures as an example, the current paper examines additive efficiency decomposition where the overall efficiency is defined as a weighted average of stage efficiencies and the weights are used to ...

A novel approach for learning of temporally extended, continuous signals is developed within the framework of rate coded neurons. A new temporal Hebb like learning rule is devised which utilizes the predictive capabilities of bandpass filtered signals by using the derivative of the output to modify the weights. The initial development of the weights is calculated analytically applying signal th...