RAM based methods

Austin, James

doi:10.1142/9789812816849_others01

Cited by 3 publications

(1 citation statement)

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“…Organoleptic evaluations of prepared bread, biscuits, noodles, and macroni were carried out by a panel of ten judges for crust colour, appearance, flavour crust texture, taste, and overall acceptability using nine point hedonic rating scale ranging from like extremely (9) to dislike extremely (1) for each organoleptic characteristics as suggested by Austin and Ram [15].…”

Section: Organoleptic Evaluationmentioning

confidence: 99%

Physicochemical, rheological, and organoleptic characteristics of wheat‐fenugreek supplemented blends

Hooda

Jood

2003

Nahrung

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The effect of blending of fenugreek (raw, soaked, and germinated) flour (Trigonellafaenum graecum) from 5 to 20% in wheat flour on the rheological and sensory evaluation of bread, biscuit, noodle, and macroni was studied. Farinograph water absorption, dough development time, mixing tolerance index, and dough stability increased significantly with increased amount of fenugreek flour. Incorporation of fenugreek flour in wheat flour increased the protein and fat contents of blends but decreased the gluten contents. Among the supplemented blends, blends containing germinated fenugreek flour had higher protein contents (13.83-16.30%) up to 20%. Overall acceptability scores of bread, biscuit, noodles, and macroni were found highly acceptable up to 15, 10, and 20% levels, respectively.

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Section: Organoleptic Evaluationmentioning

confidence: 99%

Physicochemical, rheological, and organoleptic characteristics of wheat‐fenugreek supplemented blends

Hooda

Jood

2003

Nahrung

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A Discrete Fully Recurrent Network of Max Product Units for Associative Memory and Classification

Brouwer¹

2002

Int. J. Neur. Syst.

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This paper defines the truncated normalized max product operation for the transformation of states of a network and provides a method for solving a set of equations based on this operation. The operation serves as the transformation for the set of fully connected units in a recurrent network that otherwise might consist of linear threshold units. Component values of the state vector and outputs of the units take on the values in the set [0, 0.1,..., 0.9, 1]. The result is a much larger state space given a particular number of units and size of connection matrix than for a network based on threshold units. Since the operation defined here can form the basis of transformations in a recurrent network with a finite number of states, fixed points or cycles are possible and the network based on this operation for transformations can be used as an associative memory or pattern classifier with fixed points taking on the role of prototypes. Discrete fully recurrent networks have proven themselves to be very useful as associative memories and as classifiers. However they are often based on units that have binary states. The effect of this is that the data to be processed consisting of vectors in R(n) have to be converted to vectors in [0, 1]m with m much larger than n since binary encoding based on positional notation is not feasible. This implies a large increase in the number of components. The effect can be lessened by allowing more states for each unit in our network. The network proposed demonstrates those properties that are desirable in an associative memory very well as the simulations show.

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Growing of a Fuzzy Recurrent Artificial Neural Network (Frann) for Pattern Classification

Brouwer¹

1999

Int. J. Neur. Syst.

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This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.

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RAM based methods

Cited by 3 publications

References 0 publications

Physicochemical, rheological, and organoleptic characteristics of wheat‐fenugreek supplemented blends

Physicochemical, rheological, and organoleptic characteristics of wheat‐fenugreek supplemented blends

A Discrete Fully Recurrent Network of Max Product Units for Associative Memory and Classification

Growing of a Fuzzy Recurrent Artificial Neural Network (Frann) for Pattern Classification

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